FORM 2
The Patent Act 1970
(39 of 1970)
&
The Patent Rules, 2005
COMPLETE SPECIFICATION (SEE SECTION 10 AND RULE 13)
TITLE OF THE INVENTION
“Anomaly detection system and method thereof for diagnosing plant
anomalies”
APPLICANTS:
thyssenkrupp Industries India Pvt. Ltd. 154-C, Mittal Tower, 15th Floor, 210, Nariman Point, Mumbai 400021 INDIA;
and thyssenkrupp AG, ThyssenKrupp Allee 145143 Essen, Germany.
The following specification particularly describes and ascertains the nature of this invention and the manner in which it is to be performed.

FIELD OF THE INVENTION [0001] The present disclosure relates to detecting and diagnosing plant health at an anomaly detection engine. In particular, the present invention relates to system and method for diagnosing condition, abnormalities, anomalies, and the like in a Cane Sugar plant.
BACKGROUND OF THE INVENTION
[0002] Sugar cane is an important crop and energy raw materials. Cane Sugar Plant milling section equipment’s consume a significant amount of electrical power in a sugar cane plant. Timely and accurate monitoring condition of cane preparatory devices wear parts, and its timely replacement is important for reducing power consumption in the sugar cane plant. If wear parts are not replaced in time, it increases power consumption, results in lower cane preparation and has adverse effect on downstream processes. Excessive preparation of cane is also not desirable as it increases the electrical power consumed, causes mill slippage and blocking of juice screens etc. The power costs are rising steadily, the availability of fuel is becoming scarce and exporting excess energy to the grid due to co-generation facility the consumption of power in the sugar cane processing has become a matter of great significance, as power export to grid is an important revenue stream.
[0003] Presently a mill/plant engineer/operator decides replacement of the wear parts purely based on his judgement and experience. Optimization of power and performance of any sugar plant is therefore dependent on experience of the mill/plant engineer/operator. Timely replacement of wear parts will help to reduce power consumption and increase downstream process efficiency. Reduction in power consumption can be achieved through implementation of modern computer mechanism and equipment’s. Adoption of new systems and technology for better performance and higher efficiency will help improve the economics of the sugar industry. There remains a need of a robust mechanism to predict conditions of wear parts for timely replacement to reduce the power consumption and improve overall efficiency of the plant.
[0004] Thus, it is desired to address the above mentioned disadvantages or other shortcomings or at least provide a useful alternative.

OBJECT OF THE INVENTION
[0005] The principal object of the embodiments herein is to provide a method and system for diagnosing plant anomalies.
[0006] Another object of the embodiment herein is to monitor health parameters of each of the Cane Sugar Plant milling section equipment during operation.
[0007] Yet another object of the embodiment herein is to detect an amount of wear of wear parts of each of the preparatory devices during the plant stoppage.
[0008] Yet another object of the embodiment herein is to determine a correlation between the health parameters of each of the Cane Sugar Plant milling section equipment’s and the amount of the wear of the wear parts of each of the preparatory devices by comparing the health parameters obtained during operation and the amount of the wear of the wear parts of each of the preparatory devices measured manually during the plant stoppage.
[0009] Yet another object of the embodiment herein is to detect at least one anomaly associated with at least one of the cane preparatory device based on the correlation.
[0010] Yet another object of the embodiment herein is to predict a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.
SUMMARY OF THE INVENTION [0011] Accordingly embodiments herein disclose a method for diagnosing plant anomalies. The method comprises monitoring health parameters of each of Cane Sugar Plant milling section equipment’s during operation, recording the amount of wear of wear parts of each of the preparatory devices during the plant stoppage. Further, the method comprises determining a correlation between the health parameters of each of the Cane Sugar Plant milling section equipment with the amount of the wear of the wear parts of each of the preparatory devices, and detecting at least one anomaly associated with at least one of the Cane Sugar Plant milling section equipment’s based on the correlation. Furthermore, the method comprises predicting a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.

[0012] In an embodiment, determining, by the anomaly detection system, the correlation between the health parameters, comprising performing, by the anomaly detection system, an analysis of the health parameters by comparing the health parameters obtained during operation and the amount of the wear of the wear parts of each of the preparatory devices measured manually during the plant stoppage, and determining, by the anomaly detection system, the correlation based on the analysis.
[0013] In an embodiment, the health comprises at least one of a level of vibration of each of the preparatory devices, a power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, online weighments of shredded cane, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recorder and an image of the prepared cane.
[0014] In an embodiment, the health parameters of the Cane Sugar Plant milling section equipment’s are monitored using at least one of a Belt Weigher, a kW transducer, a vibration sensor, a speed transducer, a sound recorder and an imaging sensor deployed at discharge of the shredder / fiberisor.
[0015] Another aspect of the invention provides anomaly detection system for diagnosing plant anomalies comprises a sensory system comprising a plurality of sensors deployed on at least one portion of each of the Cane Sugar Plant milling section equipment’s and an anomaly detection engine operationally coupled to the sensory system. The plurality of sensors are configured to monitor health parameters of the each of the Cane Sugar Plant milling section equipment’s. The anomaly detection engine is configured to determine a correlation between the health parameters of each of the Cane Sugar Plant milling section equipment’s with the amount of the wear of the wear parts of each of the preparatory devices, and detect at least one anomaly associated with at least one of the Cane Sugar Plant milling section equipment’s based on the correlation. Further, the anomaly detection engine is further configured to predict a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.
[0016] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the

accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF FIGURES
[0017] This method and system is illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
[0018] FIG. 1 a block diagram illustrating a high level overview of an anomaly detection system, according to an embodiment as disclosed herein;
[0019] FIG. 2 is a schematic diagram illustrating a process flow diagram of a Sugar Plant Milling section and its measurement points, according to an embodiment as disclosed herein;
[0020] FIG. 3a illustrates knives of preparatory devices indicating wear area, according to an embodiment as disclosed herein;
[0021] FIG. 3b illustrates hammers of preparatory devices indicating wear area, according to an embodiment as disclosed herein; and
[0022] FIG. 4 is a flow diagram illustrating a method for diagnosing plant anomalies, according to an embodiment as disclosed herein.

DETAILED DESCRIPTION OF THE INVENTION [0023] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term “or” as used herein, refers to a non-exclusive or, unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0024] The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.
[0025] Accordingly embodiments herein disclose a method for diagnosing plant anomalies. The method comprises monitoring health parameters of each of the Cane Sugar Plant milling section equipment’s during operation, manually measuring an amount of wear of wear parts of each of the preparatory devices during the plant stoppage. Further, the method comprises determining a correlation between the health parameters of each of the Cane Sugar Plant milling section equipment’s with the amount of the wear of the wear parts of each of the preparatory devices, and detecting at least one anomaly associated with at least one of the Cane Sugar Plant milling section equipment’s based on the correlation. Furthermore, the method comprises predicting a time at which at least one of the wear part of

at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.
[0026] Another aspect of the invention provides anomaly detection system for diagnosing plant anomalies comprises a sensory system comprising a plurality of sensors deployed on at least one portion of each of Cane Sugar Plant milling section equipment’s and an anomaly detection engine operationally coupled to the sensory system. The plurality of sensors are configured to monitor health parameters of the each of the Cane Sugar Plant milling section equipment’s. The anomaly detection engine is configured to determine a correlation between the health parameters of each of the Cane Sugar Plant milling section equipment’s with the amount of the wear of the wear parts of each of the preparatory devices, and detect at least one anomaly associated with at least one of the Cane Sugar Plant milling section equipment’s based on the correlation. Further, the anomaly detection engine is further configured to predict a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.
[0027] Unlike conventional mechanisms, the proposed anomaly detection system is used to predict and providing decision support for replacement of wear parts such as knives of leveler/chopper & hammer tips of fibrizer/shredder using real time data and data analytics as timely replacement of wear parts will help to reduce power consumption and increase downstream process efficiency. The proposed anomaly detection system helps to detect anomaly associated with at least one of the Cane Sugar Plant milling section equipment’s based on an analysis of the health parameters is performed by comparing the level of vibration of each of the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane obtained during operation and the amount of the wear of the wear parts of each of the preparatory devices measured manually during the plant stoppage. Such analysis is further useful to take decisions on timely replacement of wear parts to reduce the power consumption and improve overall efficiency of the plant.

[0028] Referring now to the drawings, and more particularly to FIGS. 1 through 4, there are shown preferred embodiments.
[0029] FIG. 1 a block diagram illustrating a high level overview of an anomaly detection system (1000), according to an embodiment as disclosed herein. The anomaly detection system (1000) comprises a sensor system (100), an anomaly detection engine (200), and a quality evaluation station (300).
[0030] The sensor system (100) comprises a plurality of sensors (110) and vibration sensors (120). The plurality of sensors (110) deployed on each of the Cane Sugar Plant milling section equipment’s. The plurality of sensors (110) are configured to monitor health parameters of the Cane Sugar Plant milling section equipment’s.
[0031] In an embodiment, the Cane Sugar Plant milling section equipment’s includes for example but not limited to cutter, chopper, leveler, fibrizer, shredder, Mills and conveyors/ carriers.
[0032] In an embodiment, the wear parts includes for example but not limited hammer tip of fibrizer/shredder, and knives of the cutter/ chopper/ leveler.
[0033] In an embodiment, the health parameters comprises a level of vibration of each of the preparatory devices, a power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane.
[0034] The information of the quality of prepared cane is used to identify the condition/anomaly of the wear parts of the cane preparatory devices. In an embodiment, the quality evaluation station (300) is for example but not limited to a digital assistance, a computer, and a wireless device.
[0035] In an embodiment, the plurality of sensors includes a Belt Weigher, a kW transducer, a speed transducer, a sound recorder and an imaging sensor deployed on the Cane Sugar Plant milling section equipment’s. For examples a Camera is installed on the conveyor after the Shredder / Fiberisor for ascertaining a preparation index of shredded cane to know the efficiency of preparatory devices on real time basis. The Belt weighters are installed on

the conveyors after the Shredder / Fiberisor and after the last mill to know a rate of the shredded cane (TPH) and a rate of the bagasse (TPH) produced respectively. The sound recorder is installed on at least one of the preparatory devices for sound signature analysis.
[0036] The vibration sensors (120) deployed on each of the cane preparatory devices
and are configured to detect the level of vibration of the cane preparatory devices. In an
embodiment, the vibration sensors (120) are installed in two planes on bearings block of all
the preparatory devices in the Cane Sugar Plant milling section to correlate an effect
vibration level of the wear parts of the preparatory devices during operation.
[0037] The quality evaluation station (300) is configured to collect cane and bagasse quality data during operation and wear of the wear parts of each of the preparatory devices during the plant stoppage.
[0038] The anomaly detection engine (200) comprises a health parameter monitor (210), a sensor data analyzer (220), an action predictor (230), and a memory (240). The health parameter monitor (210) configured to receive the health parameters of each of the Cane Sugar Plant milling section equipment’s from the plurality of sensors (110), and the quality information of prepared cane & bagasse, Cane crushed in a day, Plant stoppage time and the amount of the wear of the wear parts of each of the preparatory devices measured manually during the plant stoppage from the quality evaluation station (300). The sensor data analyzer (220) configured to determine a correlation between the health parameters of each of the Cane Sugar Plant milling section equipment’s with the amount of the wear of the wear parts of each of the preparatory devices. An analysis of the health parameters is performed by comparing the level of vibration of each of the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane and the amount of the wear of the wear parts of each of the preparatory devices manually recorded during the plant stoppage. Based on the analysis, the correlation is determined. Unlike to the conventional systems, the correlation between the wear parts of preparatory devices vis-à-vis power consumption of the Cane Sugar Plant Milling section equipment’s , the vibration

information of the preparatory devices, rate of cane prepared & bagasse produced, and quality of prepared cane and bagasse so as to be able to predict extent of wear on visually inaccessible parts and support the plant management to plan replacement of knives of CSDE & HOC and hammer tips of fibrizer/shredder.
[0039] The action predictor (230) is configured to detect at least one anomaly associated with at least one of the Cane Sugar Plant Milling equipment’s based on the correlation. Examples of the anomaly includes an abnormal behavior of the wear parts of the preparatory devices, abnormal sound of the preparatory devices, higher level of vibration, and higher level of power consumption of the preparatory devices and variations in the Mill power consumption.
[0040] Further, the action predictor (230) is configured to predict a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly. Unlike the conventional system, this helps the plant management to plan timely replacement of wear parts so as to reduce the power consumption and improve overall efficiency of the plant..
[0041] Consider an example, during the milling of cane the power is consumed by the following equipment’s:
i. Leveler / CSDE ii. Chopper / HOC iii. Cutter iv. Shredder/ Fiberiser
v. Mills; vi. Cane Carriers/ Conveyors
[0042] Various sensors such as imaging sensor, vibration sensor, KW transducer, Speed transducer, sound recorder and Belt weighers are deployed on one or more Cane Sugar Plant Milling section equipment. These deployed sensors are configured to collect real-time data of each Cane Sugar Plant Milling section equipment’s. The collected real-time data includes current health condition of the Cane Sugar Plant Milling section equipment’s during operation. An analysis of the current health parameters such as level of vibration of each of

the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane is performed in comparison with the amount of the wear of the wear parts of each of the preparatory devices to identify a correlation between the current health parameters, the level of vibration, rate and quality information of the prepared cane and bagasse produced. Based on the analysis performed, anomaly associated with at least one of the Cane Sugar Plant Milling section equipment’s is detected. This anomaly information can further be used to predict a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced. Unlike the conventional systems, the real time data or feedback about level of vibration of each of the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane , etc. can be used to effectively detect anomaly and predicts when the replacement of wear parts of preparatory devices is due since all these parameters together or separately affect the rate of wear of the wear parts of the Preparatory devices . This will help to reduce the power consumption of the Milling plant and optimise preparation of cane for desired mill results thereby increasing the efficiency of the Milling tandem and increase juice extraction from the cane.
[0043] The memory (240) stores the data received from the various sensors as per required interval. This data will be saved on the hard disk or any other storage space external or internal to the anomaly detection system (100) and can be accessed remotely. The memory (240) may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory (240) may, in some examples, be considered a non-transitory storage medium. The term “non-transitory” may indicate that

the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted that the memory (240) is non-movable. In some examples, the memory (240) can be configured to store larger amounts of information than the memory. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).
[0044] FIG. 2 is a schematic diagram illustrating a process flow diagram of a Sugar Plant Milling section and its measurement points, according to an embodiment as disclosed herein. The Sugar Cane is received from the Sugarcane farm and loaded on the cane carriers which transport the Cane into the Cane Sugar Plant Milling section, where in the beginning the received cane is chopped and levelled by the Leveller/ CSDE. In the next step the chopped and leveled cane is transported to the HOC/ Chopper where the juice containing cells are partially opened. Thereafter, the partially prepared cane is transported to the Shredder / Fiberisor where the juice containing cells of the cane are opened to the required extent. This prepared cane is then fed to the Sugar Mills where the sugar juice is extracted and after the last mill the cane fibre i.e the bagasse is taken to the yard for drying and used as a fuel to the bagasse fired Boiler. The sensors to monitor the health of the Cane Sugar Plant Milling section are installed at various one or more Milling section equipment’s. The portions at which the sensors are deployed is referred as measurement points. The various measure points are used to provide the information about level of vibration of each of the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a speed of the shredded cane conveyor, a sound recording of at least one of the preparatory devices and an image of the prepared cane to see the quality of prepared cane.
[0045] FIG. 3a illustrates knives of preparatory devices indicating wear area, according to an embodiment as disclosed herein. FIG. 3b illustrates hammers of preparatory devices indicating wear area, according to an embodiment as disclosed herein. The wear areas as indicated for the hammer tip of the shredder and knives of preparatory devices is measured and recorded manually during the plant stoppage. The “manual recording” of the amount of wear of wear parts can be performed in any way suitable to a skilled person, for example by recording the amount of wear by hand, by using suited instruments or by – fully or at least

partly – automated acquisition of the amount of wear on an individual basis but not limited to the same. One can also envisage other wear recording ways including automated or semi-automated recording as well as recording by using instruments or the like, which comes under the ambit of the term “manual recording”.
[0046] FIG. 4 is a flow diagram illustrating a method (500) for diagnosing plant anomalies, according to an embodiment as disclosed herein. At step 502, health parameters of each of Cane Sugar Plant Milling section equipment’s during operation, are monitored using the plurality of sensors (110) such as a Belt Weigher, a kW transducer, a speed transducer, a sound recorder and an imaging sensor are deployed on the Cane Sugar Plant milling section equipment’s. The health parameters comprises a level of vibration of each of the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane.
[0047] At step 504, an amount of wear of wear parts of each of the preparatory devices is measured and recorded manually during the plant stoppage. As explained above, the “manual recording” of the amount of wear of wear parts can be performed in any way suitable to a skilled person, for example by recording the amount of wear by hand, by using suited instruments or by – fully or at least partly – automated acquisition of the amount of wear on an individual basis but not limited to the same. One can also envisage other wear recording ways including automated or semi-automated recording as well as recording by using instruments or the like, which comes under the ambit of the term “manual recording”.
[0048] At step 506, an analysis of the health parameters is performed by comparing the level of vibration of each of the preparatory devices, the power consumption of each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at

least one of the preparatory devices and an image of the prepared cane obtained during operation and the amount of the wear of the wear parts of each of the preparatory devices measured and recorded manually during the plant stoppage.
[0049] At step 508, the correlation is determined based on the analysis by the anomaly detection engine (200).
[0050] At step 510, one or more anomalies associated with the Cane Sugar Plant Mill Section equipment’s is determined based on the correlation.
[0051] At step 514, a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced is predicted based on the at least one detected anomaly.
[0052] The various actions, acts, blocks, steps, or the like in the method (500) may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.
[0053] As is traditional in the field, embodiments described herein and illustrated in terms of blocks in the FIGS. 1-4 which carry out a described function or functions. These blocks, which may be referred to herein as managers, units, modules, hardware components or the like, are physically implemented by analog and/or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits and the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the disclosure. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the disclosure.

[0054] The foregoing description of the specific embodiments will 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.

We Claim:
1. A method for diagnosing plant anomalies, comprises:
monitoring, by an anomaly detection system (1000), health parameters of each of Cane sugar plant Milling section equipment’s during operation;
measuring and recording manually the amount of wear of wear parts of each of the preparatory devices during the plant stoppage;
determining, by the anomaly detection system (1000), a correlation between the health parameters of each of the Cane Sugar Plant Milling section equipment’s with the amount of the wear of the wear parts of each of the preparatory devices; and
detecting, by the anomaly detection system (1000), at least one anomaly associated with at least one of the Cane Sugar Plant Milling section equipment’s based on the correlation.
2. The method of claim 1, further comprising predicting, by the anomaly detection system (1000), a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.
3. The method of claim 1, wherein determining, by the anomaly detection system (1000), the correlation between the health parameters, comprising:
performing, by the anomaly detection system (1000), an analysis of the health parameters by comparing the health parameters obtained during operation and the amount of the wear of the wear parts of each of the preparatory devices measured and recorded manually during the plant stoppage; and
determining, by the anomaly detection system (1000), the correlation based on the analysis.
4. The method of claim 1, wherein the health comprises at least one of a level of vibration of
each of the preparatory devices, the power consumption of each of the preparatory devices
and each of the Mills, a rate of cane prepared & bagasse produced, a quality information
of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an
amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one
of the preparatory devices and an image of the prepared cane.

5. The method of claim 1, wherein the health parameters of each of Cane Sugar Plant Milling section equipment’s are monitored using at least a Belt Weigher, Vibration sensor a kW transducer, a speed transducer, a sound recorder and an imaging sensors.
6. An anomaly detection system (1000) for diagnosing plant anomalies, comprises:
a sensory system (100) comprising a plurality of sensors & vibration sensor deployed on at least one portion of each of the Cane Sugar Plant Milling section equipment’s , wherein the plurality of sensors are configured to monitor health parameters of the each of the Cane Sugar Plant Milling section equipment’s; and
a Quality Evaluation Station (300) configured to collect cane and bagasse quality data and wear of the wear parts of each of the preparatory devices during the plant stoppage ; and
an anomaly detection engine (200), operationally coupled to the sensory system, configured to:
determine a correlation between the health parameters of each of the Cane Sugar
Plant Milling section equipment’s with the amount of the wear of the wear parts of
each of the preparatory devices, and
detect at least one anomaly associated with at least one of the Cane Sugar Plant
Milling section equipment’s based on the correlation.
7. The anomaly detection system (1000) of claim 6, wherein the anomaly detection engine is further configured to predict a time at which at least one of the wear part of at least one of the cane preparatory device needs to be replaced based on the at least one detected anomaly.
8. The anomaly detection system (1000) of claim 6, wherein determine the correlation between the health parameters, comprising:
perform an analysis of the health parameters by comparing the health parameters obtained during operation and the amount of the wear parts of each of the preparatory devices during the plant stoppage; and
determine the correlation based on the analysis.
9. The anomaly detection system (1000) of claim 6, wherein the health comprises at least
one of level of vibration of each of the preparatory devices, the power consumption of

each of the preparatory devices and each of the Mills, a rate of cane prepared & bagasse produced, a quality information of the prepared cane & bagasse produced, a speed of the shredded cane conveyor, an amount of cane crushed in a day, a plant stoppage time, a sound recording of at least one of the preparatory devices and an image of the prepared cane. 10. The anomaly detection system (1000) of claim 6, wherein the health parameters of each of cane preparatory devices are monitored using at least one a Belt Weigher, Vibration sensor a kW transducer, a speed transducer, a sound recorder and an imaging sensor deployed at discharge of the Shredder / Fiberiser.