Claims:WE CLAIM:
1. A computer implemented system (100) for predicting adverse effects on equipment, said system (100) comprising:
• a memory (102) configured to store a set of pre-determined rules;
• a processor (104) configured to generate a plurality of processing commands based on said set of rules;
• a database (106) configured to store historical data, related to a plurality of pre-determined parameters, corresponding effects on said equipment due to said parameters, and a plurality of recommendations to avoid said adverse effects;
• a sensing unit (108) adapted to cooperate with said processor (104) and configured to sense current parameters related to said equipment and corresponding current condition of said equipment, based on the processing commands;
• a prediction tool (110) adapted to cooperate with said processor (104), said database (106) and said sensing unit (108), and configured to predict adverse effects on said equipment based on said sensed current parameters; and
• a display (112) configured to display information related to said predicted adverse effects, and said sensed current parameters resulting in said adverse effects.

2. The system (100) as claimed in claim 1, wherein said prediction tool (110) comprises:
• a statistical unit (110a) configured to compute correlation between said parameters and said adverse effects on equipment; and
• an effect analyzer (110b) adapted to cooperate with said statistical unit (110a), and configured to use regression techniques to analyze the effect of said parameters and compute a degradation coefficient based on said sensed current parameters.

3. The system (100) as claimed in claim 1, which further comprises:
• an alerting module (114) adapted to cooperate with said prediction tool (110), and configured to provide alerts based on said predicted adverse effects;
• an updater (116) adapted to cooperate with said sensing unit (108) and said database (106), and configured to update said historical data to include said current condition related to said sensed current parameters; and
• a recommendation module (118) adapted to cooperate with said prediction tool (110) and, configured to provide recommendations, from said database (106), based on said sensed current parameters, to avoid occurrence of said predicted adverse effects.

4. The system (100) as claimed in claim 1, wherein said parameters are selected from a group of process parameters and surrounding parameters.

5. The system (100) as claimed in claim 1, wherein said sensor unit (108) comprises a plurality of sensors selected from a group consisting of temperature sensor, IR sensor, ultrasonic sensor, proximity sensor, pressure sensor, level sensor, smoke and gas sensors, electric current sensor, radio sensor, humidity sensor, flow sensor, optical sensor, position sensor, chemical sensor, environment sensor, magnetic switch sensor, biosensors, gyroscopes, and speed sensor.

6. A computer implemented method for predicting adverse effects on equipment, said method comprising the following steps:
• storing, in a memory (102), a set of pre-determined rules;
• generating, by a processor (104), a plurality of processing commands based on said set of rules;
• storing, in a database (106), historical data related to a plurality of pre-determined parameters, corresponding effects on said equipment due to said parameters, and a plurality of recommendations to avoid said adverse effects;
• sensing, by a sensing unit (108), current parameters related to said equipment and corresponding current condition of said equipment, based on the processing commands;
• predicting, by a prediction tool (110), adverse effects on said equipment based on said sensed current parameters; and
• displaying, on a display (112), information related to said predicted adverse effects, and said sensed current parameters resulting in said adverse effects.

7. The method as claimed in claim 5, wherein said step of predicting adverse effects further comprises the following steps:
• computing correlation between said parameters and said adverse effects on equipment, by a statistical unit (110a); and
• using regression techniques, by an effect analyzer (110b), to analyze the effect of said parameters and compute a degradation coefficient based on said sensed current parameters.

8. The method as claimed in claim 5, which further comprises the following steps:
• providing alerts, by an alerting module (114), based on said predicted adverse effects;
• updating said historical data in said database (106), by an updater (116), to include said current condition related to said sensed current parameters; and
• providing recommendations from said database (106) based on said sensed current parameters, by a recommendation module (118), to avoid occurrence of said predicted adverse effects.
, Description:FIELD
The present disclosure relates to the field of adverse effect monitoring and predicting systems.
DEFINITIONS
As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used, indicate otherwise.
The expression ‘equipment’ used hereinafter in the specification refers to apparatus, articles, appliances, instruments, pipes, metallic and non-metallic objects, and other assets.
The expression ‘adverse effects’ used hereinafter in the specification refers to various effects, on equipment, such as corrosion, degradation, wear and tear, aging, and the like. Adverse effects can be induced due to various process conditions and/or due to surrounding environment of equipment.
The expression ‘current parameters’ used hereinafter in the specification refers to equipment related parameters that exist or occur at a particular instant or in real-time.
These definitions are in addition to those expressed in the art.
BACKGROUND
Generally, adverse effects on equipment are observed when the equipment is exposed over a period of time, to the surrounding environment and various process conditions. Over exposure of equipment to hazardous or adverse conditions leads to failure of equipment. Numerous economic as well as environmental risks arise due to such failure. Moreover, equipment failure may cause severe safety issues and operational hazards. In some cases, equipment failure can result in increased downtime leading to increased costs. Some organizations continuously monitor equipment condition and take action only when equipment is near failure or after it has failed. However, such approach can be unfavourable if equipment is rendered useless because of being subjected to certain adverse effects.
Hence, to limit the aforementioned drawbacks, there is a need to provide a computer implemented system that predicts adverse effects on equipment to prevent equipment failure and prolong equipment life.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
An object of the present disclosure is to provide a system that predicts adverse effects on equipment.
Another object of the present disclosure is to provide a system that can prevent untimely equipment failure.
Yet another object of the present disclosure is to provide a system which ensures that equipment is in best possible condition.
Other advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The computer implemented system for predicting adverse effects on equipment is envisaged. The system comprises a memory, a processor, a database, a sensing unit, a prediction tool, and a display. The memory stores a set of pre-determined rules, and the processor generates a plurality of processing commands based on the set of rules. The database stores historical data, related to a plurality of pre-determined parameters, corresponding effects on the equipment due to the parameters, and a plurality of recommendations to avoid the adverse effects. The sensing unit cooperates with the processor and, senses current parameters related to the equipment and corresponding current condition of the equipment, based on the processing commands. The prediction tool cooperates with the processor, the database and the sensing unit, and predicts adverse effects on the equipment based on the sensed current parameters. The display displays information related to the predicted adverse effects, and the sensed current parameters resulting in the adverse effects.
In one embodiment, the prediction tool comprises a statistical unit that computes correlation between the parameters and the adverse effects on equipment, and an effect analyzer that cooperates with the statistical unit, and uses regression techniques to analyze the effect of the parameters and compute a degradation coefficient based on the sensed current parameters.
The system further comprises an alerting module, an updater, and a recommendation module. The alerting module cooperates with the prediction tool, and provides alerts based on the predicted adverse effects. The updater cooperates with the sensing unit and the database, and updates the historical data to include the current condition related to the sensed current parameters. The recommendation module cooperates with the prediction tool and, provides recommendations from the database, based on the sensed current parameters, to avoid occurrence of the predicted adverse effects.
Further, the parameters are selected from a group of process parameters and surrounding parameters. Furthermore, the sensor unit comprises a plurality of sensors selected from a group consisting of temperature sensor, IR sensor, ultrasonic sensor, proximity sensor, pressure sensor, level sensor, smoke and gas sensors, electric current sensor, radio sensor, humidity sensor, flow sensor, optical sensor, position sensor, chemical sensor, environment sensor, magnetic switch sensor, biosensors, gyroscopes, and speed sensor.
Additionally, a computer implemented method for predicting adverse effects on equipment is also envisaged. The method comprising the following steps:
• storing, in a memory, a set of pre-determined rules;
• generating, by a processor, a plurality of processing commands based on the set of rules;
• storing, in a database, historical data related to a plurality of pre-determined parameters, corresponding effects on the equipment due to the parameters, and a plurality of recommendations to avoid the adverse effects;
• sensing, by a sensing unit, current parameters related to the equipment and corresponding current condition of the equipment, based on the processing commands;
• predicting, by a prediction tool, adverse effects on the equipment based on the sensed current parameters; and
• displaying, on a display, information related to the predicted adverse effects, and the sensed current parameters resulting in the adverse effects.
In one embodiment, the step of predicting adverse effects further comprises the following steps:
• computing correlation between the parameters and the adverse effects on equipment, by a statistical unit; and
• using regression techniques, by an effect analyzer, to analyze the effect of the parameters, and compute a degradation coefficient based on the sensed current parameters.
The method further comprises the following steps:
• providing alerts, by an alerting module, based on the predicted adverse effects;
• updating the historical data in the database, by an updater, to include the current condition related to the sensed current parameters; and
• providing recommendations from the database based on the sensed current parameters, by a recommendation module, to avoid occurrence of the predicted adverse effects.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
The computer implemented system and method for predicting adverse effects on equipment, of the present disclosure will now be described with the help of the accompanying drawing, in which:
Figure 1 illustrates a schematic block diagram of a computer implemented system for predicting adverse effects on equipment, in accordance with an embodiment of the present disclosure; and
Figure 2 illustrates a flow diagram of a computer implemented method for predicting adverse effects on equipment, in accordance with one embodiment of the present disclosure;
LIST AND DETAILS OF REFERENCE NUMERALS USED IN THE DESCRIPTION AND DRAWING:
Reference Numeral Reference
100 System for predicting adverse effects on equipment
102 Memory
104 Processor
106 Database
108 Sensing unit
110 Prediction tool
110a Statistical unit
110b Effect analyzer
112 Display
114 Alerting module
116 Updater
118 Recommendation module
200 Method for predicting adverse effects on equipment
202 - 212 Method steps

DETAILED DESCRIPTION
Generally, adverse effects on equipment are observed when the equipment is exposed over a period of time, to the surrounding environment and various process conditions. Over exposure of equipment to hazardous or adverse conditions leads to failure of equipment. Numerous economic as well as environmental risks arise due to such failure. Moreover, equipment failure may cause severe safety issues and operational hazards. In some cases, equipment failure can result in increased downtime leading to increased costs. Some organizations continuously monitor equipment condition and take action only when equipment is near failure or after it has failed. However, such approach can be unfavourable if equipment is rendered useless because of being subjected to certain adverse effects.
Hence, to limit the aforementioned drawbacks, the present disclosure envisages a computer implemented system and method for predicting adverse effects on equipment. The system and method of the present disclosure is now described with the help of non-limiting accompanying drawing. Figure 1 illustrates a schematic block diagram of a computer implemented system 100 for predicting adverse effects on equipment (hereinafter referred to as system). The system 100 comprises a memory 102, a processor 104, a database 106, a sensing unit 108, a prediction tool 110, and a display 112.
The memory 102 is configured to store a set of pre-determined rules. The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or a non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 102 is configured to store predetermined rules related to sensing different parameters of related to the equipment, rules to identify various adverse effects, rules for prediction, rules to carry out regression analysis, and the like.
The processor 104 is configured to generate a plurality of processing commands based on the set of rules. The processor 104 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 104 is configured to fetch and execute the predetermined set of rules stored in the memory 102 to control modules of the system 100.
The database 106 is configured to store historical data, related to a plurality of pre-determined parameters, corresponding effects on the equipment due to the parameters, and a plurality of recommendations to avoid the adverse effects. In one embodiment, the parameters are selected from a group of process parameters and surrounding parameters related to the equipment. The sensing unit 108 is adapted to cooperate with the processor 104, and is configured to sense current parameters related to the equipment and corresponding current condition of the equipment, based on the processing commands. In one embodiment, the sensing unit 108 comprises a plurality of sensors placed at various locations to sense different areas (target areas) of the equipment. The areas to be sensed can be based on user preference. In an embodiment, the sensor unit 108 comprises a plurality of sensors selected from a group consisting of temperature sensor, IR sensor, ultrasonic sensor, proximity sensor, pressure sensor, level sensor, smoke and gas sensors, electric current sensor, radio sensor, humidity sensor, flow sensor, optical sensor, position sensor, chemical sensor, environment sensor, magnetic switch sensor, biosensors, gyroscopes, speed sensor, and the like.
The prediction tool 110 is adapted to cooperate with the processor 104, the database 106, and the sensing unit 108, and is configured to predict adverse effects on the equipment based on the sensed current parameters. In one embodiment the prediction tool 110 comprises a statistical unit 110a and an effect analyzer 110b. The statistical unit 110a is configured to compute correlation between the parameters and the adverse effects on equipment. The effect analyzer 110b is adapted to cooperate with the statistical unit 110a, and is configured to use regression techniques to analyze the effect of the parameters, and compute a degradation coefficient based on the sensed current parameters. In one embodiment, the degradation coefficient provides criticality factor to facilitate a user to understand how the sensed current parameters are affecting the equipment. In another embodiment, the degradation coefficient can be used to identify rate with which the equipment is adversely affected by the sensed current parameters. For example, if two parts of the equipment are considered wherein degradation coefficient for a first part is ‘2’ and that of a second part is ‘5’, it denotes that the second part is getting adversely affected by the current parameters/conditions at a higher rate than the first part. The identified degradation coefficient is displayed on the display 112 along with other information related to the predicted adverse effects, and the sensed current parameters resulting in the adverse effects.
The system 100 further comprises an alerting module 114, an updater 116 adapted, and a recommendation module 118. The alerting module 114 is adapted to cooperate with the prediction tool 110, and is configured to provide alerts based on the predicted adverse effects. The updater 116 is adapted to cooperate with the sensing unit 108 and the database 106, and is configured to update the historical data to include the current condition related to the sensed current parameters. The recommendation module 118 is adapted to cooperate with the prediction tool 110 and, is configured to provide recommendations from the database 106, based on the sensed current parameters, to avoid occurrence of the predicted adverse effects.
In one exemplary embodiment, equipment is an intricate network of pipelines, used in an infrastructure at a sub-sea level, to carry fluids from sub-sea wells to an onshore facility for extracting, purification and transport of gas. These pipelines are subject to corrosion due to effect of process fluids flowing internally and due to external environment. The system 100 of the present disclosure is used to monitor health of these pipelines. The system 100 uses the sensing unit 108 having a large number of online sensors to transmit continuous data pertaining to various process parameters and corrosion levels. The database 106 includes data related to the pipelines, that has been collected for previous years. In order to predict the adverse effect on the pipelines, relevant parameters/variables, from the set of pre-determined parameters, impacting corrosion of pipelines, are selected. In this embodiment, the pre-determined parameters include salinity of sea water, metal composition in pipelines, pipeline parts exposed to the sea water and vulnerable to degradation, application of the pipelines, ideal failure rate of the parts, maintenance required, and the like. The statistical unit 110a is then used to compute correlation amongst these parameters to understand how these parameters relate to each other. For example, a part in the pipelines may be more prone to be affected by salinity of the sea water, and may thus degrade at a faster rate. Once the correlation is identified, regression techniques are used by the effect analyzer 110b to compute a degradation coefficient based on the sensed current parameters. In one embodiment, the degradation coefficient provides the extent with which the sensed current parameters affect the pipelines adversely. The degradation coefficient, i.e., prediction along with key parameters causing the adverse effects, and relevant notifications are displayed on the display 112. This information alerts operational personnel to take appropriate actions. The recommendation module 118 can also provide recommendations related to appropriate steps that need to be carried out to ensure maximum equipment integrity. In this, the recommendations stored in the database 106 are provided to a user based in the sensed current parameters. Further, different areas/sections on the pipelines can be specifically sensed to predict future adverse effect rates of different sections of the assets using predictive analytics techniques. The system 100 crunches real time data from multiple sources and pin-points sections of equipment which may be at a higher risk in the future so that necessary preventive steps can be taken to eliminate such risks.
Figure 2 of the accompanying drawing illustrates a flow diagram of a computer implemented method for predicting adverse effects on equipment 200 (hereinafter referred to as method). The method 200 comprises the following steps:
• storing, in a memory 102, a set of pre-determined rules; (step 202)
• generating, by a processor 104, a plurality of processing commands based on the set of rules; (step 204)
• storing, in a database 106, historical data related to a plurality of pre-determined parameters, corresponding effects on the equipment due to the parameters, and a plurality of recommendations to avoid the adverse effects; (step 206)
• sensing, by a sensing unit 108, current parameters related to the equipment and corresponding current condition of the equipment, based on the processing commands; (step 208)
• predicting, by a prediction tool 110, adverse effects on the equipment based on the sensed current parameters; (step 210) and
• displaying, on a display 112, information related to the predicted adverse effects, and the sensed current parameters resulting in the adverse effects. (step 212)
In one embodiment, the step of predicting adverse effects (step 210) further comprises the following steps:
• computing correlation between the parameters and the adverse effects on equipment, by a statistical unit 110a; and
• using regression techniques, by an effect analyzer 110b, to analyze the effect of the parameters and compute a degradation coefficient based on the sensed current parameters.
Further, the method 200 also comprises the following steps:
• providing alerts, by an alerting module (114), based on the predicted adverse effects;
• updating the historical data in the database 106, by an updater (116), to include the current condition related to the sensed current parameters; and
• providing recommendations from the database 106 based on the sensed current parameters, by a recommendation module (118), to avoid occurrence of the predicted adverse effects.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a computer implemented system and method for predicting adverse effects on equipment, that:
• can prevent untimely equipment failure;
• can prolong equipment life; and
• ensures that equipment is in best possible condition.
The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
The numerical values mentioned for the various physical parameters, dimensions or quantities are only approximations and it is envisaged that the values higher/lower than the numerical values assigned to the parameters, dimensions or quantities fall within the scope of the disclosure, unless there is a statement in the specification specific to the contrary.
While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.