1. A method for reducing pipe failure rate (107) in a Water Distribution Network (WDN) 101, the method comprising: determining, by a failure prediction system (201), real-time hydraulic pressure (205) of water being supplied through the pipe based on a target volume of water (223) to be supplied through the pipe; estimating, by the failure prediction system (201), a pipe failure rate (209) of the pipe based on one or more pipe deployment parameters (225) associated with the pipe and the real-time hydraulic pressure (205); comparing, by the failure prediction system (201), the pipe failure rate (209) with predetermined pipe failure rate to determine a deviation in the pipe failure rate (209); and varying, by the failure prediction system (201), the real-time hydraulic pressure (205) of water in the pipe due to the deviation in the pipe failure rate (209) for reducing the pipe failure rate (107) in the WDN (101).

2. The method as claimed in claim 1, wherein the one or more pipe deployment parameters (225) include at least one of length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe.

3. The method as claimed in claim 1, wherein the predetermined pipe failure rate is determined based on values of the one or more pipe deployment parameters (225) and a predetermined hydraulic pressure limit of water to be supplied through the pipe.

4. The method as claimed in claim 1, wherein varying the real-time hydraulic pressure (205) of the water is based on the target volume of water (223).

5. The method as claimed in claim 4, wherein the real-time hydraulic pressure (205) is varied by varying angle of a supply control valve associated with the pipe.

6. The method as claimed in claim 1 further comprises performing at least one of replacement or repair of the pipe when the pipe failure rate (209) of the pipe is higher than the predetermined pipe failure rate.

7. A failure prediction system (201) for reducing pipe failure rate (107) in a Water Distribution Network (WDN) 101, the failure prediction system (201) comprising: a processor (215); and a memory (217) communicatively coupled to the processor (215), wherein the memory (217) stores processor-executable instructions, which on execution, causes the processor (215) to: determine real-time hydraulic pressure (205) of water being supplied through the pipe based on a target volume of water (223) to be supplied through the pipe; estimate pipe failure rate (209) of the pipe based on one or more pipe deployment parameters (225) associated with the pipe and the real-time hydraulic pressure (205); compare the pipe failure rate (209) with predetermined pipe failure rate to determine a deviation in the pipe failure rate (209); and vary the real-time hydraulic pressure (205) of water in the pipe due to the deviation in the pipe failure rate (209) for reducing the pipe failure rate (107) in the WDN (101).

8. The failure prediction system (201) as claimed in claim 7, wherein the one or more pipe deployment parameters (225) include at least one of length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe.

9. The failure prediction system (201) as claimed in claim 7, wherein the instructions cause the processor (215) to determine the predetermined pipe failure rate based on values of the one or more pipe deployment parameters (225) and a predetermined hydraulic pressure limit of water to be supplied through the pipe.

10. The failure prediction system (201) as claimed in claim 7, wherein the instructions cause the processor (215) to vary the real-time hydraulic pressure (205) of the water based on the target volume of water (223).

11. The failure prediction system (201) as claimed in claim 10, wherein processor (215) varies the real-time hydraulic pressure (205) by varying angle of a supply control valve associated with the pipe.

12. The failure prediction system (201) as claimed in claim 7, wherein the instructions further cause the processor (215) to perform at least one of replacement or repair of the pipe when the pipe failure rate (209) of the pipe is higher than the predetermined pipe failure rate. , Description:TECHNICAL FIELD The present subject matter is related, in general to monitoring resource distribution systems, and more particularly, but not exclusively to a method and system for reducing pipe failure rate in a Water Distribution Network (WDN). BACKGROUND Generally, any Water Distribution Network (WDN) having an ageing pipe infrastructure are plagued by leaks, which in turn leads to inadequacy of water supply compared to the demand across each node of the WDN. These problems are among major challenges faced by water utilities for accounting and reducing Non-Revenue Water (NRW) in the WDN. Currently, the water utilities adopt either a pipe replacement strategy or a pressure control logic for managing the leaks in the pipes and to reduce wastage of water across the WDN. However, each of these measures have their own advantages and limitations. While replacement of the pipe, eliminates leaks completely, it is a cost intensive and time-consuming solution. Similarly, the application of pressure control logic may be an economically feasible option, but it cannot eliminate leaks in the pipes. Further, there is no clear insight as to whether pressure control is a viable option to reduce the pipe failure rate before replacing or repairing the pipes, which is based on estimated values of the pipe failure rate. Moreover, as indicated in FIG. 1, which is an exemplary illustration 100 of existing policies for Operation and Maintenance (O&M) of the WDN 101. The pipe failure rate 107 is estimated based on a pipe failure rate estimation logic 105 that uses historic data 104 obtained from a historical database 103 associated with the WDN 101. These estimations may be inaccurate as there are frequent pressure transients in the WDN 101 depending on the demand pattern. This is an important criterion for determining pipe failure rate 107. Therefore, the O&M strategy 109 for repair and/or replacement of the pipe based on the number of failures is not accurate. Moreover, the existing O&M policies 109 do not include effects of pressure control as a viable option to reduce the pipe failure rate 107. SUMMARY Disclosed herein is a method for reducing pipe failure rate in a Water Distribution Network (WDN). The method comprises determining, by a failure prediction system, real-time hydraulic pressure of water being supplied through the pipe based on a target volume of water to be supplied through the pipe. Further, the method comprises estimating a pipe failure rate of the pipe based on one or more pipe deployment parameters associated with the pipe and the real-time hydraulic pressure. Upon estimating the pipe failure rate, the method comprises comparing the pipe failure rate with predetermined pipe failure rate to determine a deviation in the pipe failure rate. Finally, the method comprises varying the real-time hydraulic pressure of water in the pipe due to the deviation in the pipe failure rate for reducing the pipe failure rate in the WDN. Further, the present disclosure discloses a failure prediction system for reducing pipe failure rate in a Water Distribution Network (WDN). The failure prediction system comprises a processor and a memory. The memory is communicatively coupled to the processor and stores processor-executable instructions. The instructions, upon execution, cause the processor to determine real-time hydraulic pressure of water being supplied through the pipe based on a target volume of water to be supplied through the pipe. Further, the processor estimates a pipe failure rate of the pipe based on one or more pipe deployment parameters associated with the pipe and the real-time hydraulic pressure. Upon determining the pipe failure rate, the processor compares the pipe failure rate with predetermined pipe failure rate to determine a deviation in the pipe failure rate. Finally, the instructions cause the processor to vary the real-time hydraulic pressure of water in the pipe due to the deviation in the pipe failure rate for reducing the pipe failure rate in the WDN. The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which: FIG. 1 illustrates an existing method for determining pipe failure rate in accordance with some of the existing embodiments in a similar area of research; FIG. 2A illustrates an exemplary environment for determining a pipe failure rate in accordance with some embodiments of the present disclosure; FIG. 2B shows a detailed block diagram illustrating a failure prediction system for determining the pipe failure rate in accordance with some embodiments of the present disclosure; FIGS. 3A and 3B indicate dependency among supply time and water level, and hydraulic pressure and supply volume of water in accordance with some exemplary embodiment of the present disclosure; FIG. 4 shows a flowchart illustrating a method of determining the pipe failure rate in accordance with some embodiments of the present disclosure; and FIG. 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown. DETAILED DESCRIPTION In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure. The terms “comprises”, “comprising”, “includes”, “including” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. The present disclosure relates to a method and a failure prediction system for determining pipe failure rate across and to reduce the pipe failure rate in a Water Distribution Network (WDN). The present disclosure proposes a decision support system for optimal Operation and Maintenance of the WDN. The method uses a real-time hydraulic Pressure Driven Demand (PDD) model to determine the real-time hydraulic pressure of the pipe. Further, a pressure control strategy is implemented across the WDN to accurately regulate a pressure relief valve in the WDN based on minimization of error/deviation in the values of real-time hydraulic pressure. In some embodiments, the method of present disclosure estimates a pipe failure rate across the WDN as a function of the real-time hydraulic pressure. Later, one or more corrective operations are performed on the WDN based on the estimated value of the pipe failure rate. As an example, if the pipe failure rate is less than a predetermined pipe failure rate, then no corrective operation is performed in the WDN. On the other hand, if the pipe failure rate is greater than the predetermined pipe failure rate, then one or more pipes in the WDN may be repaired or replaced based on a cost function. In some embodiments of the present disclosure, incorporating the effect of pressure control during estimation of the pipe failure rate enhances accuracy of estimation of the real-time hydraulic pressure. Further, by reducing the hydraulic pressure across one or more pipes of the WDN helps in reducing the pipe failure rate, thereby reducing costs associated with repair and/or replacement of pipes in the WDN. In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. FIG. 2A illustrates an exemplary environment 200 for determining a pipe failure rate 209 across a Water Distribution Network (WDN) 101 in accordance with some embodiments of the present disclosure. Accordingly, the environment 200 includes the WDN 101, a historical database 103 associated with the WDN 101, a Hydraulic Pressure Driven Demand (PDD) model 203 associated with the WDN 101 and a failure prediction system 201. In an embodiment, the WDN 101 may include a source of water such as a water reservoir or a water treatment plant and one or more supply zones or supply nodes that require supply of water. As an example, each of the one or more supply zones may represent a locality such as, District Metered Areas (DMAs) and the one or more nodes within the supply zones 108 may be household connections and other end points that require and use water from the source of water. In some embodiments, the water from the source of water may be supplied to the one or more supply zones through one or more water distribution pipes/channels in the WDN 101. In some embodiments, the historical database 103 may store information related to the WDN 101. As an example, the historical database 103 may store details related to number of pipes in the WDN 101, demand across the one or more nodes in the WDN 101 and hydraulic pressure through the pipes. Further, the historical database 103 may store one or more pipe deployment parameters 225 including, without limiting to, length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe. In some embodiments, the hydraulic PDD model 203 that incorporates a correlation between the hydraulic pressure and the demand for water across the WDN 101. In other words, the hydraulic PDD model 203helps in establishing a simultaneous control over the demand and the pressure across the one or more nodes of the WDN 101. In the present disclosure, the hydraulic PDD model 203 may be used to determine a real-time hydraulic pressure 205 of water being supplied through the pipes in the WDN 101. In some embodiments, the failure prediction system 201 may determine the real-time hydraulic pressure 205 based on a target volume of water 223 (demand) to be supplied to the one or more nodes, which is determined in real-time by the hydraulic PDD model 203. Further, the failure prediction system 201 may estimate a pipe failure rate 209 in the WDN 101 based on the one or more pipe deployment parameters 225 and other historic data 104 obtained from the historical database 103. In some embodiments, upon determining the pipe failure rate 209 of the pipe, the failure prediction system 201 may compare the determined pipe failure rate 209 with a predetermined pipe failure rate for determining a deviation in the pipe failure rate 209. In some implementations, the predetermined pipe failure rate may be computed as a function of the one or more pipe deployment parameters 225 and a standard hydraulic pressure across the pipe. Additionally, the failure prediction system 201 may compare the real-time hydraulic pressure 205 of the water due to the deviation in the pipe failure rate 209 for reducing the pipe failure rate 209 in the WDN 101 based on the target volume of water 233. As an example, if the pipe failure rate 209 is less than the predetermined pipe failure rate, then the real-time hydraulic pressure 205 may be marginally increased in order to supply the target volume of water 223 to be supplied through the pipe. On the other hand, if the pipe failure rate 209 is higher than the predetermined pipe failure rate, then the real-time hydraulic pressure 205 may be reduced in order to reduce hydraulic tension in the pipe, thereby improving life of the pipe and reducing the overall pipe failure rate in the WDN 101. In some embodiments, existing policies for Operation and Maintenance (O&M) of the WDN 101 may be optimized by application of the real-time hydraulic pressure 205 for reducing the pipe failure rate 209 of the pipe. Thus, the optimized O&M strategies 211 disclosed in the present disclosure help in determining a correct time to either repair or replace a leaky pipe in the WDN 101 by the estimation of pipe failure rate 209. FIG. 2B shows a detailed block diagram illustrating a failure prediction system 201 for determining the pipe failure rate 209 in a Water Distribution Network (WDN) 101 in accordance with some embodiments of the present disclosure. The failure prediction system 201 may include an I/O interface 213, a processor 215 and a memory 217. The I/O interface 213 may be used for receiving one or more pipe deployment parameters 225 and other historic data 104 from a historical database 103 associated with the WDN 101. The memory 217 may be communicatively coupled to the processor 215. The processor 215 may be configured to perform one or more functions of the failure prediction system 201 for determining the pipe failure rate 209 in the WDN 101. In one implementation, the failure prediction system 201 may include data 219 and modules 221, which are used for performing various operations in accordance with the embodiments of the present disclosure. In an embodiment, the data 219 may be stored within the memory 217 and may include, without limiting to, a target volume of water 223, the one or more pipe deployment parameters 225, real-time hydraulic pressure 205, the pipe failure rate 209, and other data 229. In some embodiments, the data 219 may be stored within the memory 217 in the form of various data structures. Additionally, the data 219 may be organized using data models, such as relational or hierarchical data models. The other data 229 may store data, including temporary data and temporary files, generated by modules 221 while determining the pipe failure rate 209. In some embodiment, the target volume of water 223 (or supply volume) may be the demand for water across the one or more nodes of the WDN 101. In some implementations, the target volume of water 223 may be determined using the hydraulic PDD model 203 associated with the WDN 101. Further, the failure prediction system 201 may determine the real-time hydraulic pressure 205 of water flowing through the pipe based on correlation between the target volume of water 223 using the hydraulic PDD model 203. In some embodiments, the one or more pipe deployment parameters 225 may include, without limiting to, length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe. The one or more pipe deployment parameters 225 may be received from the historical database 103. Further, values of each of the one or more pipe deployment parameters 225 may vary depending on the nature and use of the pipe. For example, the diameter of a pipe that supplies water to a household point (domestic node) may be less than the diameter of a pipe that supplies water from a source node to a distribution node used for distributing the water to multiple domestic nodes. In an embodiment, the pipe failure rate 107 of the pipe may be directly proportional to the age of the pipe. In some embodiments, the real-time hydraulic pressure 205 may be determined based on the target volume of water 223 to be supplied through the pipe. Further, the real-time hydraulic pressure 205 through the pipe may be varied by varying angle of a supply control valve associated with the pipe. In an implementation, the angle of the supply control valve may be directly proportional to the hydraulic pressure of water flowing through the pipe. i.e., increasing the angle of the supply control valve may result in an increase in the volume of water flowing through the pipe and hence, an increase in the hydraulic pressure in the pipe. In some embodiments, the pipe failure rate 209 may be estimated as a function of the one or more pipe deployment parameters 225 and the real-time hydraulic pressure 205. The pipe failure rate 209 may indicate an estimated life of the pipe. As an example, the pipe failure rate 209 may be computed as a function of an equation (1) shown below: Pipe failure rate 209 = F (D, L, Age, De, P real-time) … (1) Where, ‘D’ is the Diameter of the pipe; ‘L’ is the Length of the pipe; ‘Age’ is the number of years since the deployment of the pipe; ‘De’ is the Depth of deployment of the pipe; and ‘P real-time’ is the real-time hydraulic pressure 205 in the pipe. For example, the pipe failure rate 209 may be computed using the below formula – (1A) which is derived from the equation (1) above. Pipe failure rate 209 = - 0.4197 * (D0.3762) + 0.4168 * (L0.0872) + 0.2813 * (P 0.5668) + 0.0903 * (De-1) + 0.7408 * (Age0.4281) … (1A) As an example, the diameter of the pipe may be 80 mm, the length of the pipe may be 100 m, age of the pipe may be 20 years, the depth of installation of the pipe may be 1.2m and the pressure of water in the pipe may be 2 atm. Based on the above values being substituted in equation (1A), the pipe failure rate 209 calculated is 1.60. However, upon implementing the present disclosure, the pressure of water in the pipe is varied from 2 atm to 1atm based on target volume of the water 233. By substituting the varied pressure value i.e 1 atm in the equation (1A), the pipe failure rate 209 calculated is 1.46, thereby reducing the pipe failure rate 209. In some embodiments, as indicated in equation (1) and formula (1A), the pipe failure rate 209 may be directly proportional to the ‘P real-time’, which means that, the pipe failure rate 209 of a pipe may be reduced by reducing the real-time hydraulic pressure 205 on the pipe. In some embodiments, the data 219 may be processed by one or more modules 221 in the failure prediction system 201. In one implementation, the one or more modules 221 may be stored as a part of the processor 215. In another implementation, the one or more modules 221 may be communicatively coupled to the processor 215 for performing one or more functions of the failure prediction system 201. The modules 221 may include, without limiting to, a failure estimation module 231, a deviation detection module 233, an Operation and Maintenance module 235, and other modules 237. As used herein, the term ‘module’ may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an embodiment, the other modules 237 may be used to perform various miscellaneous functionalities of the failure prediction system 201. It will be appreciated that such modules 221 may be represented as a single module or a combination of different modules. In some embodiments, the failure rate estimation module 231 may be responsible for estimating the pipe failure rate 209 of the pipe using the equation (1). In some implementations, the failure rate estimation module 231 may receive the one or more pipe deployment parameters 225 associated with the pipe from the historical database 103. Further, the failure rate estimation module 231 may be interfaced with each of one or more supply valves in the WDN 101 to revive the real-time hydraulic pressure 205 in the pipe. In some embodiments, the deviation detection module 233 may be responsible for determining a deviation in the pipe failure rate 209 with respect to the predetermined pipe failure rate of the pipe. The predetermined pipe failure rate may be a constant value that indicates the approximate pipe failure rate 209 of a pipe, as prescribed by a manufacturer of the pipe and/or a certification authority. The value of predetermined pipe failure rate may be different for each of the one or more pipes in the WDN 101, and each of these values may be stored in the historical database 103. In some embodiments, the deviation in the pipe failure rate 209 may be determined to be positive when the pipe failure rate 209 is less than the predetermined pipe failure rate. Similarly, the deviation may be determined to be negative when the pipe failure rate 209 is higher than the predetermined pipe failure rate. Further, the failure prediction system 201 may reduce the hydraulic pressure of the pipe when the deviation is negative. In some implementations, the Operation and Maintenance (O&M) module may be responsible for performing one or more corrective operations on the one or more pipes in the WDN 101 based on the deviation in the pipe failure rate 209. As an example, the one or more corrective operations may include at least one of, without limiting to, repairing the pipe or replacing the pipe. In some implementations, each of the one or more corrective operations to be performed by the O&M module may be controlled based on a cost function associated with the WDN 101. The cost function may determine the approximate costs associated with repairing and/or replacing the pipe as indicated in equations (2) and (3) below: Cost of repairing = Number of breaks in the pipe * L * RPc … (2) Where, ‘Number of breaks’ is the total number of leaks, holes or breakages in the pipe; ‘L’ is the length of the pipe; and ‘RPc’ indicates the approximate costs involved in repairing each break; i.e., the cost of repairing a leaky and/or a damaged pipe may be directly proportional to the number of breaks in the pipe, length of the pipe and the repairing cost for each break in the pipe. Cost of replacement = Number of breaks in the pipe * L * RPLc … (3) Where, ‘Number of breaks’ is the total number of leaks, holes or breakages in the pipe; ‘L’ is the length of the pipe; and ‘RPLc’ indicates the approximate costs involved in replacing a protein of pipe which is broken; i.e., the cost of replacing a leaky and/or a damaged pipe may be directly proportional to the number of breaks in the pipe, length of the pipe and the replacement cost for each broken portion of the pipe. In an embodiment, the number of breaks in the pipe is determined based on the pipe failure rate 209. In some embodiments, the O&M module 235 decides to either repair or replace the pipe may be determined based on the cost of repairing and replacing. Further, if the deviation in the pipe failure rate is positive, no corrective operations may be performed on the one or more pipes in the WDN 101. In some embodiments, the O&M module 235 may recommend the one or more corrective operations to a user and/or a supervisor of the WDN 101 through a user interface (shown in FIG. 5) associated with the failure prediction system 201. Alternatively, the one or more corrective operations may be dynamically communicated to the user/supervisor through one or more user devices associated with the user/supervisor. FIG. 3A indicates relationship between supply time 305 and volume of water supplied in accordance with some exemplary embodiment of the present disclosure. As an example, consider a water tank placed at one of the one or more nodes of the WDN 101. Suppose, maximum capacity of water that can be stored in the water tank be 100 Liters. Now, as indicated in the graph 301, the water level 303 in the water tank may be directly proportional to the supply time 305. In some embodiments, the water level 303 in the water tank may be indicated by curves 1, 2 and 3 in the graph 301. The curve 3 indicates a condition wherein the water tank has become full (i.e., the water level 303 in the tank is 100%) before completion of the estimated supply time 305. This condition may occur when the hydraulic pressure 315 with which the water is being supplied is higher than the required hydraulic pressure. Alternatively, filling-up of the water tank may be delayed when the hydraulic pressure 315 of the water being supplied is less than the required hydraulic pressure. However, the water tank may be filled exactly at the estimated supply time 305 when the hydraulic pressure 315 of the water being supplied is equal to the required hydraulic pressure. The relationship between the water level 303 (supply volume or demand), the supply time 305 and the hydraulic pressure 315 across the pipe, as depicted in graph 301, may be established using the hydraulic PDD model 203 associated with the WDN 101. Using the hydraulic PDD model 203 helps in determining the real-time hydraulic pressure 205 which is required to fill the water tank in the given supply time 305. FIG. 3B indicates relationship between supply volume 313 and the hydraulic pressure 315 in accordance with some exemplary embodiments of the present disclosure. The relationship among the supply volume 313 and the hydraulic pressure 315 of water being supplied to the one or more nodes in the WDN 101 may be deduced from the hydraulic PDD model 203 associated with the WDN 101. As indicated in graph 310, the supply volume 313 may be directly proportional to the hydraulic pressure 315 of water in the pipe. As an example, the supply volume 313 corresponding to different values of the hydraulic pressure 315 may be indicated by points 1, 2 and 3 on the graph 310. When the hydraulic pressure 315 is less than the required hydraulic pressure 315, the supply volume 313 would be less than the demand 317. Alternatively, when the hydraulic pressure 315 is higher than the required hydraulic pressure, the supply volume 313 would be more than the demand 317. However, the supply volume 313 may exactly match with the demand 317 across the one or more nodes when the hydraulic pressure 315 value is equal to the required hydraulic pressure value. Thus, the hydraulic pressure 315 with which the water must be supplied may be determined as a function of the demand 317 across the one or more nodes, as deduced from the hydraulic PDD model 203. FIG. 4 shows a flowchart illustrating a method of determining pipe failure rate 209 in a Water Distribution Network (WDN) 101 in accordance with some embodiments of the present disclosure. As illustrated in FIG. 4, the method 400 includes one or more blocks illustrating a method for determining the pipe failure rate 209 of one or more pipes in the WDN 101 using a failure prediction system 201. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement abstract data types. The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. At block 401, the method 400 includes determining, by the failure prediction system 201, real-time hydraulic pressure 205 of water being supplied through the pipe based on a target volume of water 223 to be supplied through the pipe. In some embodiments, the target volume of water 223 to be supplied may be determined using a hydraulic PDD model 203 associated with the WDN 101. The hydraulic PDD model 203 helps in establishing a simultaneous control over the demand 317 and the pressure across the one or more nodes of the WDN 101. At block 403, the method 400 includes estimating, by the failure prediction system 201, a pipe failure rate 209 of the pipe based on one or more pipe deployment parameters 225 associated with the pipe and the real-time hydraulic pressure 205. As an example, the one or more pipe deployment parameters 225 may include without limiting to, at least one of length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe. At block 405, the method 400 includes comparing, by the failure prediction system 201, the pipe failure rate 209 with predetermined pipe failure rate to determine a deviation in the pipe failure rate 209. In an embodiment, the predetermined pipe failure rate may be determined based on values of the one or more pipe deployment parameters 225 and a predetermined hydraulic pressure limit of water to be supplied through the pipe. At block 407, the method 400 includes varying, by the failure prediction system 201, the real-time hydraulic pressure 205 of water in the pipe based on the deviation in the pipe failure rate 209 for reducing the pipe failure rate 107 in the Water Distribution Network (WDN) 101. In an embodiment, varying the real-time hydraulic pressure 205 of the water may be based on the target volume of water 223. As an example, the real-time hydraulic pressure 205 may be varied by varying angle of a supply control valve associated with the pipe. In an embodiment, at least one of replacement or repair of the pipe may be performed when the pipe failure rate 209 of the pipe is higher than a predetermined pipe failure rate. Computer System FIG. 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 may be failure prediction system 201, which may be used for determining pipe failure rate 209 in a Water Distribution Network (WDN) 101. The computer system 500 may include a central processing unit (“CPU” or “processor”) 502. The processor 502 may include at least one data processor for executing program components for executing user- or system-generated business processes. A user may include a person, a person in-charge of the WDN 101, a customer, a person using a device such as those included in this invention, or such a device itself. The processor 502 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor 502 may be disposed in communication with one or more input/output (I/O) devices (511 and 512) via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices (511 and 512). In some embodiments, the processor 502 may be disposed in communication with a communication network 509 via a network interface 503. The network interface 503 may communicate with the communication network 509. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network interface 503 and the communication network 509, the computer system 500 may communicate with a historical database 103 associated with the WDN 101 for collecting one or more data related to the WDN 101. Further, the communication network 509 may be used to provide one or more recommendations related to optimal operation and maintenance of the WDN 101 based on the pipe failure rate 209. In some implementations, the one or more recommendations may be provided through a user interface 520 associated with the failure computer system 500. The communication network 509 can be implemented as one of the distinct types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 509 may either be a dedicated network or a shared network, which represents an association of the diverse types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 509 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM 513, ROM 514, etc. as shown in FIG. 5) via a storage interface 504. The storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc. The memory 505 may store a collection of program or database components, including, without limitation, user/application data 506, an operating system 507, web server 508 etc. In some embodiments, computer system 500 may store user/application data 506, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase. The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, Net BSD, Open BSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, K-Ubuntu, etc.), International Business Machines (IBM) OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry Operating System (OS), or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 500, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like. In some embodiments, the computer system 500 may implement a web browser 508 stored program component. The web browser may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS) secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as Active Server Pages (ASP), ActiveX, American National Standards Institute (ANSI) C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), Microsoft Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 500 may implement a mail client stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc. Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media. Advantages of the embodiment of the present disclosure are illustrated herein. In an embodiment, the method of present disclosure determines pipe failure rate of one or more pipes in a Water Distribution Network (WDN), thereby facilitating maintenance of the WDN in real-time. In an embodiment, the method of present disclosure uses a Pressure Dirven Demand (PDD) model to determine a real-time hydraulic pressure of water flowing through the one or more pipes to improve accuracy in estimation of the pipe failure rate. In an embodiment, the method of present disclosure establishes a decision support system for management of an ageing pipe by incorporating effect of pressure control in prediction of the pipe failure rate. In an embodiment, the method of present disclosure helps in reducing the pipe failure rate by reducing hydraulic pressure across one or more pipes in the WDN, thereby resulting in reduced repair and/or replacement costs involved in the WDN. The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise. The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise. The enumerated listing of items does not imply that any or all the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise. A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself. Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. Referral Numerals: Reference Number Description 100 Environment consistent with existing art 101 Water Distribution Network (WDN) 103 Historical database 104 Historic data 105 Pipe failure rate estimation logic 107 Pipe failure rate 109 Operation and Maintenance strategy 200 Environment consistent with present disclosure 201 Failure prediction system 203 Hydraulic Pressure Driven Demand (PDD) model 205 Real-time hydraulic pressure 209 Pipe failure rate 211 Optimized Operation and maintenance strategy 213 I/O Interface 215 Processor 217 Memory 219 Data 221 Modules 223 Target volume of water 225 Pipe deployment parameters 229 Other data 231 Failure rate estimation module 233 Deviation detection module 235 Operation and Maintenance module 237 Other modules 303 Water level 305 Supply time 313 Supply volume 315 Hydraulic pressure 317 Demand 500 Computer system 501 I/O Interface of the computer system 502 Processor of the computer system 503 Network Interface 504 Storage Interface 505 Memory of the computer system 506 User/Application 507 Operating system 508 Web interface 509 Communication network 513 RAM 514 ROM 520 User interface

Claims:1. A method for reducing pipe failure rate (107) in a Water Distribution Network (WDN) 101, the method comprising:
determining, by a failure prediction system (201), real-time hydraulic pressure (205) of water being supplied through the pipe based on a target volume of water (223) to be supplied through the pipe;
estimating, by the failure prediction system (201), a pipe failure rate (209) of the pipe based on one or more pipe deployment parameters (225) associated with the pipe and the real-time hydraulic pressure (205);
comparing, by the failure prediction system (201), the pipe failure rate (209) with predetermined pipe failure rate to determine a deviation in the pipe failure rate (209); and
varying, by the failure prediction system (201), the real-time hydraulic pressure (205) of water in the pipe due to the deviation in the pipe failure rate (209) for reducing the pipe failure rate (107) in the WDN (101).

2. The method as claimed in claim 1, wherein the one or more pipe deployment parameters (225) include at least one of length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe.

3. The method as claimed in claim 1, wherein the predetermined pipe failure rate is determined based on values of the one or more pipe deployment parameters (225) and a predetermined hydraulic pressure limit of water to be supplied through the pipe.

4. The method as claimed in claim 1, wherein varying the real-time hydraulic pressure (205) of the water is based on the target volume of water (223).

5. The method as claimed in claim 4, wherein the real-time hydraulic pressure (205) is varied by varying angle of a supply control valve associated with the pipe.

6. The method as claimed in claim 1 further comprises performing at least one of replacement or repair of the pipe when the pipe failure rate (209) of the pipe is higher than the predetermined pipe failure rate.

7. A failure prediction system (201) for reducing pipe failure rate (107) in a Water Distribution Network (WDN) 101, the failure prediction system (201) comprising:
a processor (215); and
a memory (217) communicatively coupled to the processor (215), wherein the memory (217) stores processor-executable instructions, which on execution, causes the processor (215) to:
determine real-time hydraulic pressure (205) of water being supplied through the pipe based on a target volume of water (223) to be supplied through the pipe;
estimate pipe failure rate (209) of the pipe based on one or more pipe deployment parameters (225) associated with the pipe and the real-time hydraulic pressure (205);
compare the pipe failure rate (209) with predetermined pipe failure rate to determine a deviation in the pipe failure rate (209); and
vary the real-time hydraulic pressure (205) of water in the pipe due to the deviation in the pipe failure rate (209) for reducing the pipe failure rate (107) in the WDN (101).

8. The failure prediction system (201) as claimed in claim 7, wherein the one or more pipe deployment parameters (225) include at least one of length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe.

9. The failure prediction system (201) as claimed in claim 7, wherein the instructions cause the processor (215) to determine the predetermined pipe failure rate based on values of the one or more pipe deployment parameters (225) and a predetermined hydraulic pressure limit of water to be supplied through the pipe.

10. The failure prediction system (201) as claimed in claim 7, wherein the instructions cause the processor (215) to vary the real-time hydraulic pressure (205) of the water based on the target volume of water (223).

11. The failure prediction system (201) as claimed in claim 10, wherein processor (215) varies the real-time hydraulic pressure (205) by varying angle of a supply control valve associated with the pipe.

12. The failure prediction system (201) as claimed in claim 7, wherein the instructions further cause the processor (215) to perform at least one of replacement or repair of the pipe when the pipe failure rate (209) of the pipe is higher than the predetermined pipe failure rate.
, Description:TECHNICAL FIELD
The present subject matter is related, in general to monitoring resource distribution systems, and more particularly, but not exclusively to a method and system for reducing pipe failure rate in a Water Distribution Network (WDN).

BACKGROUND
Generally, any Water Distribution Network (WDN) having an ageing pipe infrastructure are plagued by leaks, which in turn leads to inadequacy of water supply compared to the demand across each node of the WDN. These problems are among major challenges faced by water utilities for accounting and reducing Non-Revenue Water (NRW) in the WDN. Currently, the water utilities adopt either a pipe replacement strategy or a pressure control logic for managing the leaks in the pipes and to reduce wastage of water across the WDN.

However, each of these measures have their own advantages and limitations. While replacement of the pipe, eliminates leaks completely, it is a cost intensive and time-consuming solution. Similarly, the application of pressure control logic may be an economically feasible option, but it cannot eliminate leaks in the pipes. Further, there is no clear insight as to whether pressure control is a viable option to reduce the pipe failure rate before replacing or repairing the pipes, which is based on estimated values of the pipe failure rate.

Moreover, as indicated in FIG. 1, which is an exemplary illustration 100 of existing policies for Operation and Maintenance (O&M) of the WDN 101. The pipe failure rate 107 is estimated based on a pipe failure rate estimation logic 105 that uses historic data 104 obtained from a historical database 103 associated with the WDN 101. These estimations may be inaccurate as there are frequent pressure transients in the WDN 101 depending on the demand pattern. This is an important criterion for determining pipe failure rate 107. Therefore, the O&M strategy 109 for repair and/or replacement of the pipe based on the number of failures is not accurate. Moreover, the existing O&M policies 109 do not include effects of pressure control as a viable option to reduce the pipe failure rate 107.

SUMMARY
Disclosed herein is a method for reducing pipe failure rate in a Water Distribution Network (WDN). The method comprises determining, by a failure prediction system, real-time hydraulic pressure of water being supplied through the pipe based on a target volume of water to be supplied through the pipe. Further, the method comprises estimating a pipe failure rate of the pipe based on one or more pipe deployment parameters associated with the pipe and the real-time hydraulic pressure. Upon estimating the pipe failure rate, the method comprises comparing the pipe failure rate with predetermined pipe failure rate to determine a deviation in the pipe failure rate. Finally, the method comprises varying the real-time hydraulic pressure of water in the pipe due to the deviation in the pipe failure rate for reducing the pipe failure rate in the WDN.

Further, the present disclosure discloses a failure prediction system for reducing pipe failure rate in a Water Distribution Network (WDN). The failure prediction system comprises a processor and a memory. The memory is communicatively coupled to the processor and stores processor-executable instructions. The instructions, upon execution, cause the processor to determine real-time hydraulic pressure of water being supplied through the pipe based on a target volume of water to be supplied through the pipe. Further, the processor estimates a pipe failure rate of the pipe based on one or more pipe deployment parameters associated with the pipe and the real-time hydraulic pressure. Upon determining the pipe failure rate, the processor compares the pipe failure rate with predetermined pipe failure rate to determine a deviation in the pipe failure rate. Finally, the instructions cause the processor to vary the real-time hydraulic pressure of water in the pipe due to the deviation in the pipe failure rate for reducing the pipe failure rate in the WDN.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

FIG. 1 illustrates an existing method for determining pipe failure rate in accordance with some of the existing embodiments in a similar area of research;

FIG. 2A illustrates an exemplary environment for determining a pipe failure rate in accordance with some embodiments of the present disclosure;

FIG. 2B shows a detailed block diagram illustrating a failure prediction system for determining the pipe failure rate in accordance with some embodiments of the present disclosure;

FIGS. 3A and 3B indicate dependency among supply time and water level, and hydraulic pressure and supply volume of water in accordance with some exemplary embodiment of the present disclosure;

FIG. 4 shows a flowchart illustrating a method of determining the pipe failure rate in accordance with some embodiments of the present disclosure; and

FIG. 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.
The terms “comprises”, “comprising”, “includes”, “including” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.
The present disclosure relates to a method and a failure prediction system for determining pipe failure rate across and to reduce the pipe failure rate in a Water Distribution Network (WDN). The present disclosure proposes a decision support system for optimal Operation and Maintenance of the WDN. The method uses a real-time hydraulic Pressure Driven Demand (PDD) model to determine the real-time hydraulic pressure of the pipe. Further, a pressure control strategy is implemented across the WDN to accurately regulate a pressure relief valve in the WDN based on minimization of error/deviation in the values of real-time hydraulic pressure.
In some embodiments, the method of present disclosure estimates a pipe failure rate across the WDN as a function of the real-time hydraulic pressure. Later, one or more corrective operations are performed on the WDN based on the estimated value of the pipe failure rate. As an example, if the pipe failure rate is less than a predetermined pipe failure rate, then no corrective operation is performed in the WDN. On the other hand, if the pipe failure rate is greater than the predetermined pipe failure rate, then one or more pipes in the WDN may be repaired or replaced based on a cost function.
In some embodiments of the present disclosure, incorporating the effect of pressure control during estimation of the pipe failure rate enhances accuracy of estimation of the real-time hydraulic pressure. Further, by reducing the hydraulic pressure across one or more pipes of the WDN helps in reducing the pipe failure rate, thereby reducing costs associated with repair and/or replacement of pipes in the WDN.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
FIG. 2A illustrates an exemplary environment 200 for determining a pipe failure rate 209 across a Water Distribution Network (WDN) 101 in accordance with some embodiments of the present disclosure.

Accordingly, the environment 200 includes the WDN 101, a historical database 103 associated with the WDN 101, a Hydraulic Pressure Driven Demand (PDD) model 203 associated with the WDN 101 and a failure prediction system 201. In an embodiment, the WDN 101 may include a source of water such as a water reservoir or a water treatment plant and one or more supply zones or supply nodes that require supply of water. As an example, each of the one or more supply zones may represent a locality such as, District Metered Areas (DMAs) and the one or more nodes within the supply zones 108 may be household connections and other end points that require and use water from the source of water. In some embodiments, the water from the source of water may be supplied to the one or more supply zones through one or more water distribution pipes/channels in the WDN 101.

In some embodiments, the historical database 103 may store information related to the WDN 101. As an example, the historical database 103 may store details related to number of pipes in the WDN 101, demand across the one or more nodes in the WDN 101 and hydraulic pressure through the pipes. Further, the historical database 103 may store one or more pipe deployment parameters 225 including, without limiting to, length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe.

In some embodiments, the hydraulic PDD model 203 that incorporates a correlation between the hydraulic pressure and the demand for water across the WDN 101. In other words, the hydraulic PDD model 203helps in establishing a simultaneous control over the demand and the pressure across the one or more nodes of the WDN 101. In the present disclosure, the hydraulic PDD model 203 may be used to determine a real-time hydraulic pressure 205 of water being supplied through the pipes in the WDN 101.

In some embodiments, the failure prediction system 201 may determine the real-time hydraulic pressure 205 based on a target volume of water 223 (demand) to be supplied to the one or more nodes, which is determined in real-time by the hydraulic PDD model 203. Further, the failure prediction system 201 may estimate a pipe failure rate 209 in the WDN 101 based on the one or more pipe deployment parameters 225 and other historic data 104 obtained from the historical database 103.

In some embodiments, upon determining the pipe failure rate 209 of the pipe, the failure prediction system 201 may compare the determined pipe failure rate 209 with a predetermined pipe failure rate for determining a deviation in the pipe failure rate 209. In some implementations, the predetermined pipe failure rate may be computed as a function of the one or more pipe deployment parameters 225 and a standard hydraulic pressure across the pipe. Additionally, the failure prediction system 201 may compare the real-time hydraulic pressure 205 of the water due to the deviation in the pipe failure rate 209 for reducing the pipe failure rate 209 in the WDN 101 based on the target volume of water 233. As an example, if the pipe failure rate 209 is less than the predetermined pipe failure rate, then the real-time hydraulic pressure 205 may be marginally increased in order to supply the target volume of water 223 to be supplied through the pipe. On the other hand, if the pipe failure rate 209 is higher than the predetermined pipe failure rate, then the real-time hydraulic pressure 205 may be reduced in order to reduce hydraulic tension in the pipe, thereby improving life of the pipe and reducing the overall pipe failure rate in the WDN 101.

In some embodiments, existing policies for Operation and Maintenance (O&M) of the WDN 101 may be optimized by application of the real-time hydraulic pressure 205 for reducing the pipe failure rate 209 of the pipe. Thus, the optimized O&M strategies 211 disclosed in the present disclosure help in determining a correct time to either repair or replace a leaky pipe in the WDN 101 by the estimation of pipe failure rate 209.

FIG. 2B shows a detailed block diagram illustrating a failure prediction system 201 for determining the pipe failure rate 209 in a Water Distribution Network (WDN) 101 in accordance with some embodiments of the present disclosure.

The failure prediction system 201 may include an I/O interface 213, a processor 215 and a memory 217. The I/O interface 213 may be used for receiving one or more pipe deployment parameters 225 and other historic data 104 from a historical database 103 associated with the WDN 101. The memory 217 may be communicatively coupled to the processor 215. The processor 215 may be configured to perform one or more functions of the failure prediction system 201 for determining the pipe failure rate 209 in the WDN 101. In one implementation, the failure prediction system 201 may include data 219 and modules 221, which are used for performing various operations in accordance with the embodiments of the present disclosure. In an embodiment, the data 219 may be stored within the memory 217 and may include, without limiting to, a target volume of water 223, the one or more pipe deployment parameters 225, real-time hydraulic pressure 205, the pipe failure rate 209, and other data 229.

In some embodiments, the data 219 may be stored within the memory 217 in the form of various data structures. Additionally, the data 219 may be organized using data models, such as relational or hierarchical data models. The other data 229 may store data, including temporary data and temporary files, generated by modules 221 while determining the pipe failure rate 209.

In some embodiment, the target volume of water 223 (or supply volume) may be the demand for water across the one or more nodes of the WDN 101. In some implementations, the target volume of water 223 may be determined using the hydraulic PDD model 203 associated with the WDN 101. Further, the failure prediction system 201 may determine the real-time hydraulic pressure 205 of water flowing through the pipe based on correlation between the target volume of water 223 using the hydraulic PDD model 203.

In some embodiments, the one or more pipe deployment parameters 225 may include, without limiting to, length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe. The one or more pipe deployment parameters 225 may be received from the historical database 103. Further, values of each of the one or more pipe deployment parameters 225 may vary depending on the nature and use of the pipe. For example, the diameter of a pipe that supplies water to a household point (domestic node) may be less than the diameter of a pipe that supplies water from a source node to a distribution node used for distributing the water to multiple domestic nodes. In an embodiment, the pipe failure rate 107 of the pipe may be directly proportional to the age of the pipe.

In some embodiments, the real-time hydraulic pressure 205 may be determined based on the target volume of water 223 to be supplied through the pipe. Further, the real-time hydraulic pressure 205 through the pipe may be varied by varying angle of a supply control valve associated with the pipe. In an implementation, the angle of the supply control valve may be directly proportional to the hydraulic pressure of water flowing through the pipe. i.e., increasing the angle of the supply control valve may result in an increase in the volume of water flowing through the pipe and hence, an increase in the hydraulic pressure in the pipe.

In some embodiments, the pipe failure rate 209 may be estimated as a function of the one or more pipe deployment parameters 225 and the real-time hydraulic pressure 205. The pipe failure rate 209 may indicate an estimated life of the pipe. As an example, the pipe failure rate 209 may be computed as a function of an equation (1) shown below:

Pipe failure rate 209 = F (D, L, Age, De, P real-time) … (1)

Where,
‘D’ is the Diameter of the pipe;
‘L’ is the Length of the pipe;
‘Age’ is the number of years since the deployment of the pipe;
‘De’ is the Depth of deployment of the pipe; and
‘P real-time’ is the real-time hydraulic pressure 205 in the pipe.

For example, the pipe failure rate 209 may be computed using the below formula – (1A) which is derived from the equation (1) above.

Pipe failure rate 209 = - 0.4197 * (D0.3762) + 0.4168 * (L0.0872) + 0.2813 * (P 0.5668) + 0.0903 * (De-1) + 0.7408 * (Age0.4281) … (1A)
As an example, the diameter of the pipe may be 80 mm, the length of the pipe may be 100 m, age of the pipe may be 20 years, the depth of installation of the pipe may be 1.2m and the pressure of water in the pipe may be 2 atm. Based on the above values being substituted in equation (1A), the pipe failure rate 209 calculated is 1.60. However, upon implementing the present disclosure, the pressure of water in the pipe is varied from 2 atm to 1atm based on target volume of the water 233. By substituting the varied pressure value i.e 1 atm in the equation (1A), the pipe failure rate 209 calculated is 1.46, thereby reducing the pipe failure rate 209.

In some embodiments, as indicated in equation (1) and formula (1A), the pipe failure rate 209 may be directly proportional to the ‘P real-time’, which means that, the pipe failure rate 209 of a pipe may be reduced by reducing the real-time hydraulic pressure 205 on the pipe.

In some embodiments, the data 219 may be processed by one or more modules 221 in the failure prediction system 201. In one implementation, the one or more modules 221 may be stored as a part of the processor 215. In another implementation, the one or more modules 221 may be communicatively coupled to the processor 215 for performing one or more functions of the failure prediction system 201. The modules 221 may include, without limiting to, a failure estimation module 231, a deviation detection module 233, an Operation and Maintenance module 235, and other modules 237.

As used herein, the term ‘module’ may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an embodiment, the other modules 237 may be used to perform various miscellaneous functionalities of the failure prediction system 201. It will be appreciated that such modules 221 may be represented as a single module or a combination of different modules.

In some embodiments, the failure rate estimation module 231 may be responsible for estimating the pipe failure rate 209 of the pipe using the equation (1). In some implementations, the failure rate estimation module 231 may receive the one or more pipe deployment parameters 225 associated with the pipe from the historical database 103. Further, the failure rate estimation module 231 may be interfaced with each of one or more supply valves in the WDN 101 to revive the real-time hydraulic pressure 205 in the pipe.

In some embodiments, the deviation detection module 233 may be responsible for determining a deviation in the pipe failure rate 209 with respect to the predetermined pipe failure rate of the pipe. The predetermined pipe failure rate may be a constant value that indicates the approximate pipe failure rate 209 of a pipe, as prescribed by a manufacturer of the pipe and/or a certification authority. The value of predetermined pipe failure rate may be different for each of the one or more pipes in the WDN 101, and each of these values may be stored in the historical database 103.

In some embodiments, the deviation in the pipe failure rate 209 may be determined to be positive when the pipe failure rate 209 is less than the predetermined pipe failure rate. Similarly, the deviation may be determined to be negative when the pipe failure rate 209 is higher than the predetermined pipe failure rate. Further, the failure prediction system 201 may reduce the hydraulic pressure of the pipe when the deviation is negative.

In some implementations, the Operation and Maintenance (O&M) module may be responsible for performing one or more corrective operations on the one or more pipes in the WDN 101 based on the deviation in the pipe failure rate 209. As an example, the one or more corrective operations may include at least one of, without limiting to, repairing the pipe or replacing the pipe. In some implementations, each of the one or more corrective operations to be performed by the O&M module may be controlled based on a cost function associated with the WDN 101. The cost function may determine the approximate costs associated with repairing and/or replacing the pipe as indicated in equations (2) and (3) below:

Cost of repairing = Number of breaks in the pipe * L * RPc … (2)

Where,
‘Number of breaks’ is the total number of leaks, holes or breakages in the pipe;
‘L’ is the length of the pipe; and
‘RPc’ indicates the approximate costs involved in repairing each break;
i.e., the cost of repairing a leaky and/or a damaged pipe may be directly proportional to the number of breaks in the pipe, length of the pipe and the repairing cost for each break in the pipe.
Cost of replacement = Number of breaks in the pipe * L * RPLc … (3)

Where,
‘Number of breaks’ is the total number of leaks, holes or breakages in the pipe;
‘L’ is the length of the pipe; and
‘RPLc’ indicates the approximate costs involved in replacing a protein of pipe which is broken;

i.e., the cost of replacing a leaky and/or a damaged pipe may be directly proportional to the number of breaks in the pipe, length of the pipe and the replacement cost for each broken portion of the pipe. In an embodiment, the number of breaks in the pipe is determined based on the pipe failure rate 209.

In some embodiments, the O&M module 235 decides to either repair or replace the pipe may be determined based on the cost of repairing and replacing. Further, if the deviation in the pipe failure rate is positive, no corrective operations may be performed on the one or more pipes in the WDN 101. In some embodiments, the O&M module 235 may recommend the one or more corrective operations to a user and/or a supervisor of the WDN 101 through a user interface (shown in FIG. 5) associated with the failure prediction system 201. Alternatively, the one or more corrective operations may be dynamically communicated to the user/supervisor through one or more user devices associated with the user/supervisor.

FIG. 3A indicates relationship between supply time 305 and volume of water supplied in accordance with some exemplary embodiment of the present disclosure.

As an example, consider a water tank placed at one of the one or more nodes of the WDN 101. Suppose, maximum capacity of water that can be stored in the water tank be 100 Liters. Now, as indicated in the graph 301, the water level 303 in the water tank may be directly proportional to the supply time 305. In some embodiments, the water level 303 in the water tank may be indicated by curves 1, 2 and 3 in the graph 301. The curve 3 indicates a condition wherein the water tank has become full (i.e., the water level 303 in the tank is 100%) before completion of the estimated supply time 305. This condition may occur when the hydraulic pressure 315 with which the water is being supplied is higher than the required hydraulic pressure.

Alternatively, filling-up of the water tank may be delayed when the hydraulic pressure 315 of the water being supplied is less than the required hydraulic pressure. However, the water tank may be filled exactly at the estimated supply time 305 when the hydraulic pressure 315 of the water being supplied is equal to the required hydraulic pressure. The relationship between the water level 303 (supply volume or demand), the supply time 305 and the hydraulic pressure 315 across the pipe, as depicted in graph 301, may be established using the hydraulic PDD model 203 associated with the WDN 101. Using the hydraulic PDD model 203 helps in determining the real-time hydraulic pressure 205 which is required to fill the water tank in the given supply time 305.

FIG. 3B indicates relationship between supply volume 313 and the hydraulic pressure 315 in accordance with some exemplary embodiments of the present disclosure.

The relationship among the supply volume 313 and the hydraulic pressure 315 of water being supplied to the one or more nodes in the WDN 101 may be deduced from the hydraulic PDD model 203 associated with the WDN 101. As indicated in graph 310, the supply volume 313 may be directly proportional to the hydraulic pressure 315 of water in the pipe. As an example, the supply volume 313 corresponding to different values of the hydraulic pressure 315 may be indicated by points 1, 2 and 3 on the graph 310. When the hydraulic pressure 315 is less than the required hydraulic pressure 315, the supply volume 313 would be less than the demand 317. Alternatively, when the hydraulic pressure 315 is higher than the required hydraulic pressure, the supply volume 313 would be more than the demand 317. However, the supply volume 313 may exactly match with the demand 317 across the one or more nodes when the hydraulic pressure 315 value is equal to the required hydraulic pressure value. Thus, the hydraulic pressure 315 with which the water must be supplied may be determined as a function of the demand 317 across the one or more nodes, as deduced from the hydraulic PDD model 203.

FIG. 4 shows a flowchart illustrating a method of determining pipe failure rate 209 in a Water Distribution Network (WDN) 101 in accordance with some embodiments of the present disclosure.

As illustrated in FIG. 4, the method 400 includes one or more blocks illustrating a method for determining the pipe failure rate 209 of one or more pipes in the WDN 101 using a failure prediction system 201. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement abstract data types.

The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 401, the method 400 includes determining, by the failure prediction system 201, real-time hydraulic pressure 205 of water being supplied through the pipe based on a target volume of water 223 to be supplied through the pipe. In some embodiments, the target volume of water 223 to be supplied may be determined using a hydraulic PDD model 203 associated with the WDN 101. The hydraulic PDD model 203 helps in establishing a simultaneous control over the demand 317 and the pressure across the one or more nodes of the WDN 101.

At block 403, the method 400 includes estimating, by the failure prediction system 201, a pipe failure rate 209 of the pipe based on one or more pipe deployment parameters 225 associated with the pipe and the real-time hydraulic pressure 205. As an example, the one or more pipe deployment parameters 225 may include without limiting to, at least one of length of the pipe, diameter of the pipe, age of the pipe, and depth of deployment of the pipe.

At block 405, the method 400 includes comparing, by the failure prediction system 201, the pipe failure rate 209 with predetermined pipe failure rate to determine a deviation in the pipe failure rate 209. In an embodiment, the predetermined pipe failure rate may be determined based on values of the one or more pipe deployment parameters 225 and a predetermined hydraulic pressure limit of water to be supplied through the pipe.

At block 407, the method 400 includes varying, by the failure prediction system 201, the real-time hydraulic pressure 205 of water in the pipe based on the deviation in the pipe failure rate 209 for reducing the pipe failure rate 107 in the Water Distribution Network (WDN) 101. In an embodiment, varying the real-time hydraulic pressure 205 of the water may be based on the target volume of water 223. As an example, the real-time hydraulic pressure 205 may be varied by varying angle of a supply control valve associated with the pipe. In an embodiment, at least one of replacement or repair of the pipe may be performed when the pipe failure rate 209 of the pipe is higher than a predetermined pipe failure rate.

Computer System
FIG. 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 may be failure prediction system 201, which may be used for determining pipe failure rate 209 in a Water Distribution Network (WDN) 101. The computer system 500 may include a central processing unit (“CPU” or “processor”) 502. The processor 502 may include at least one data processor for executing program components for executing user- or system-generated business processes. A user may include a person, a person in-charge of the WDN 101, a customer, a person using a device such as those included in this invention, or such a device itself. The processor 502 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
The processor 502 may be disposed in communication with one or more input/output (I/O) devices (511 and 512) via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc.
Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices (511 and 512). In some embodiments, the processor 502 may be disposed in communication with a communication network 509 via a network interface 503. The network interface 503 may communicate with the communication network 509. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.
Using the network interface 503 and the communication network 509, the computer system 500 may communicate with a historical database 103 associated with the WDN 101 for collecting one or more data related to the WDN 101. Further, the communication network 509 may be used to provide one or more recommendations related to optimal operation and maintenance of the WDN 101 based on the pipe failure rate 209. In some implementations, the one or more recommendations may be provided through a user interface 520 associated with the failure computer system 500. The communication network 509 can be implemented as one of the distinct types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 509 may either be a dedicated network or a shared network, which represents an association of the diverse types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network 509 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.
In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM 513, ROM 514, etc. as shown in FIG. 5) via a storage interface 504. The storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
The memory 505 may store a collection of program or database components, including, without limitation, user/application data 506, an operating system 507, web server 508 etc. In some embodiments, computer system 500 may store user/application data 506, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.
The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, Net BSD, Open BSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, K-Ubuntu, etc.), International Business Machines (IBM) OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry Operating System (OS), or the like. A user interface may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 500, such as cursors, icons, check boxes, menus, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), or the like.
In some embodiments, the computer system 500 may implement a web browser 508 stored program component. The web browser may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS) secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as Active Server Pages (ASP), ActiveX, American National Standards Institute (ANSI) C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), Microsoft Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 500 may implement a mail client stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
Advantages of the embodiment of the present disclosure are illustrated herein.
In an embodiment, the method of present disclosure determines pipe failure rate of one or more pipes in a Water Distribution Network (WDN), thereby facilitating maintenance of the WDN in real-time.

In an embodiment, the method of present disclosure uses a Pressure Dirven Demand (PDD) model to determine a real-time hydraulic pressure of water flowing through the one or more pipes to improve accuracy in estimation of the pipe failure rate.

In an embodiment, the method of present disclosure establishes a decision support system for management of an ageing pipe by incorporating effect of pressure control in prediction of the pipe failure rate.

In an embodiment, the method of present disclosure helps in reducing the pipe failure rate by reducing hydraulic pressure across one or more pipes in the WDN, thereby resulting in reduced repair and/or replacement costs involved in the WDN.

The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the invention(s)" unless expressly specified otherwise.

The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

The enumerated listing of items does not imply that any or all the items are mutually exclusive, unless expressly specified otherwise.

The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise. A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Referral Numerals:
Reference Number Description
100 Environment consistent with existing art
101 Water Distribution Network (WDN)
103 Historical database
104 Historic data
105 Pipe failure rate estimation logic
107 Pipe failure rate
109 Operation and Maintenance strategy
200 Environment consistent with present disclosure
201 Failure prediction system
203 Hydraulic Pressure Driven Demand (PDD) model
205 Real-time hydraulic pressure
209 Pipe failure rate
211 Optimized Operation and maintenance strategy
213 I/O Interface
215 Processor
217 Memory
219 Data
221 Modules
223 Target volume of water
225 Pipe deployment parameters
229 Other data
231 Failure rate estimation module
233 Deviation detection module
235 Operation and Maintenance module
237 Other modules
303 Water level
305 Supply time
313 Supply volume
315 Hydraulic pressure
317 Demand
500 Computer system
501 I/O Interface of the computer system
502 Processor of the computer system
503 Network Interface
504 Storage Interface
505 Memory of the computer system
506 User/Application
507 Operating system
508 Web interface
509 Communication network
513 RAM
514 ROM
520 User interface