SYSTEM, APPARATUS AND METHOD FOR MANAGING AN ELECTROMECHANICAL SYSTEM

Abstract

A system, apparatus and method for managing an electromechanical system is provided. In an embodiment, the method includes receiving, by a processing unit, operational data associated with the electromechanical system from one or more sensing units in real-time. Furthermore, the method includes detecting an event associated with a power supplied to the electromechanical system based on the operational data. Also, the method includes configuring a virtual replica of the electromechanical system using the operational data, based on the event detected. Additionally, the method further includes generating one or more simulation results by simulating the configured virtual replica in a simulation environment. Moreover, the method includes predicting an effect on the electromechanical system due to the event based on the simulation results.

Claims

1. A computer-implemented method for managing an electromechanical system, the method comprising: receiving, by a processing unit, operational data associated with the electromechanical system from one or more sensing units in real-time; detecting an event associated with a power supplied to the electromechanical system based on the operational data; configuring a virtual replica of the electromechanical system using the operational data, based on the event detected; generating one or more simulation results by simulating the virtual replica in a simulation environment; predicting an effect on the electromechanical system due to the event based on the one or more simulation results; and generating a notification indicating the effect on the electromechanical system due to the event.

2. The method according to claim 1, wherein detecting the event associated with the power supplied to the electromechanical system based on the operational data comprises: determining the event from at least one parameter in the operational data using a correlation model, wherein the correlation model correlates parameter to the event in the power supplied to the electromechanical system.

3. The method according to claim 1, wherein the event is associated with at least one of a voltage unbalance and a current unbalance in the power supply.

4. The method according to claim 1, wherein predicting the effect on the electromechanical system due to the event based on the one or more simulation results comprises: analyzing a behaviour of the electromechanical system in real-time based on the one or more simulation results; and predicting the effect on the electromechanical system based on a of the electromechanical system.

5. The method according to claim 1, wherein the effect on the electromechanical system due to the event is associated with a performance of the electromechanical system.

6. The method according to claim 4, wherein predicting the effect on the electromechanical system based on the behavior of the electromechanical system comprises: computing an output of the electromechanical system by analyzing the one or more simulation results; and detecting a presence of torque ripples in an output of the electromechanical system in real-time.

7. The method according to claim 1, wherein the effect on the electromechanical system due to the event is associated with damage to a component of the electromechanical system.

8. The method according to claim 4, wherein predicting the effect on the electromechanical system based on the behavior comprises: computing a thermal profile of the component based on the behavior; detecting a presence of one or more hotspots in the component based on the thermal profile; and determining a damage to the component based on the presence of the one or more hotspots.

9. The method according to claim 1, further comprising: predicting a remaining useful life of the electromechanical system based on the effect on the electromechanical system due to the event.

10. The method according to claim 1, further comprising: determining a maintenance action to be performed on the electromechanical system based on the effect on the electromechanical system due to the event.

11. The method according to claim 1, further comprising: scheduling the maintenance action in order to optimize a down-time associated with the electromechanical system.

12. An apparatus for managing an electromechanical system, the apparatus comprising: one or more processing units; and a memory unit communicatively coupled to the one or more processing units wherein the memory unit comprises a management module stored in the form of machine-readable instructions executable by the one or more processing units, wherein the management module is configured to perform method steps according to claim 1.

13. A system for managing an electromechanical system comprising: one or more sensing units configured for providing operational data associated with electromechanical system; and an apparatus according to claim 12, communicatively coupled to the one or more sensing units, wherein the apparatus is configured to manage the electromechanical system based on the operational data.

14. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method according to claim 1.

15. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform a method according to claim 1.

Description

BRIEF DESCRIPTION

[0033] Some of the embodiments will be described in detail, with reference to the following figures, wherein like designations denote like members, wherein:

[0034] FIG. 1 illustrates a block diagram of a system for managing an electromechanical system, in accordance with an embodiment of the present invention;

[0035] FIG. 2 illustrates a set-up for calibration of a virtual replica of the electromechanical system, in accordance with an embodiment of the present invention;

[0036] FIG. 3A illustrates a Graphical User Interface showing transient temperature distribution of the electromechanical system for balanced voltages, in accordance with an embodiment of the present invention;

[0037] FIG. 3B illustrates a Graphical User Interface showing transient temperature distribution of the electromechanical system for unbalanced voltages, in accordance with an embodiment of the present invention;

[0038] FIG. 4 illustrates a Graphical User Interface showing an electric circuit model of the electromechanical system, in accordance with an embodiment of the present invention;

[0039] FIG. 5 is a flowchart depicting an exemplary method for managing an electromechanical system, in accordance with an embodiment of the present invention;

[0040] FIG. 6 is flowchart depicting an exemplary method for detecting an event associated with a power supplied to the electromechanical system, in accordance with an embodiment of the present invention;

[0041] FIG. 7 is a flowchart depicting an method for predicting effect of the voltage unbalance on a performance of the electromechanical system, in accordance with an embodiment of the present invention;

[0042] FIG. 8A is a flowchart depicting an method for predicting damage to components of the electromechanical system in real-time, in accordance with an embodiment of the present invention;

[0043] FIG. 8B is a flowchart depicting a method for generating recommendations for optimizing energy consumption of the electromechanical system, in accordance with an embodiment of the present invention. and

[0044] FIG. 9 is a flowchart depicting an exemplary method for determining a maintenance action on the electromechanical system, in accordance with an embodiment of the present invention

[0045] Hereinafter, embodiments for carrying out the present invention are described in detail. The various embodiments are described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident that such embodiments may be practiced without these specific details.

DETAILED DESCRIPTION

[0046] Referring to FIG. 1, a system 100 for managing a squirrel-cage induction motor 105 is described, in accordance with an embodiment of the present disclosure. The squirrel-cage induction motor 105 may be hereinafter referred to as ‘motor 105’. The motor 105 is powered from a three-phase power supply comprising three phases, namely, U-phase, V-phase and W-phase. The three-phase power supply may be hereinafter referred to as the power supply.

[0047] The system 100 comprises an apparatus 110 for managing the motor 105. The apparatus 110 is communicatively coupled to a plurality of sensing units 115, over a network 120. Each sensing unit among the plurality of sensing units 115 measure at least one parameter associated with an operation of the motor 105. In an embodiment, the system 100 comprises a first sensing unit 115A for capturing a magnetic signature associated with the motor 105. The first sensing unit 115A may comprise, for example, a Gauss meter for measuring stray magnetic flux associated with the motor 105. The system 100 further comprises a second sensing unit 115B for capturing a temperature signature associated with the motor 105. The second sensing unit 115B may comprise, for example, a resistance temperature detectors (RTD) positioned on a stator casing of the motor 105 for capturing a surface temperature of the stator casing. The system 100 further comprises a third sensing unit 115C for capturing a load profile of the motor 105. The third sensing unit 115C may comprise, for example, a load cell mounted on a hydraulic actuator associated with the motor 105. The system 100 further comprises a fourth sensing unit 115D for measuring torque at an output of the motor 105. The fourth sensing unit 115D may comprise, for example, a torque sensor coupled to a shaft of the motor 105. In a further embodiment, the system 100 may comprise a fifth sensing unit 115E for measuring voltage and current values associated with the power supply. The fifth sensing unit 115E may be a motor protection device. The motor protection device may be configured for calculating phase unbalances based on voltages across different phases in the power supply. Similarly, the motor protection device may also calculate the voltage unbalances in the power supply based on line-voltages across different lines in the power supply. Similarly, the motor protection device may also calculate current unbalances based on line-currents in the power supply.

[0048] The first sensing unit 115A, the second sensing unit 115B, the third sensing unit 115C, the fourth sensing unit 115D and the fifth sensing unit 115E further provide operational data comprising the measured values of each of the parameters to a controller 117.

[0049] The controller 117 comprises a trans-receiver (not shown), one or more processors (not shown) and a memory (not shown). The trans-receiver is configured to connect the controller 117 to a network interface (not shown) associated with the network 120. In one embodiment, the controller 120 receives operational data from the plurality of sensing units 115 and transmits the operational data to the apparatus 105 through the network interface.

[0050] The apparatus 110 may be a (personal) computer, a workstation, a virtual machine running on host hardware, a microcontroller, or an integrated circuit. As an alternative, the apparatus 110 may be a real or a virtual group of computers (the technical term for a real group of computers is “cluster”, the technical term for a virtual group of computers is “cloud”).

[0051] The apparatus 110 includes a communication unit 125, one or more processing units 130, a display 135, a Graphical User Interface (GUI) 140 and a memory 145 communicatively coupled to each other. In one embodiment, the communication unit 125 includes a transmitter (not shown), a receiver (not shown) and Gigabit Ethernet port (not shown). The memory 145 may include 2 Giga byte Random Access Memory (RAM) Package on Package (PoP) stacked and Flash Storage. The processing unit 130 are configured to execute the defined computer program instructions in the modules. Further, the processing unit 130 are also configured to execute the instructions in the memory 145 simultaneously. The display 135 includes a High-Definition Multimedia Interface (HDMI) display and a cooling fan (not shown). Additionally, control personnel may access the apparatus 110 through the GUI 140. The GUI 140 may include a web-based interface, a web-based downloadable application interface, and so on.

[0052] The processing unit 130, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, microcontroller, complex instruction set computing microprocessor, reduced instruction set computing microprocessor, very long instruction word microprocessor, explicitly parallel instruction computing microprocessor, graphics processor, digital signal processor, or any other type of processing circuit. The processing unit 130 may also include embedded controllers, such as generic or programmable logic devices or arrays, application specific integrated circuits, single-chip computers, and the like. In general, a processing unit 130 may comprise hardware elements and software elements. The processing unit 130 can be configured for multithreading, i.e., the processing unit 130 may host different calculation processes at the same time, executing the either in parallel or switching between active and passive calculation processes.

[0053] The memory 145 may include one or more of a volatile memory and a non-volatile memory. The memory 145 may be coupled for communication with the processing unit 130. The processing unit 130 may execute instructions and/or code stored in the memory 145. A variety of computer-readable storage media may be stored in and accessed from the memory 145. The memory 145 may include any suitable elements for storing data and machine-readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like.

[0054] In the present embodiment, the memory 145 includes a calibration module 150, a preprocessing module 155, an event detection module 160, a configuration module 165, a simulation module 170, an analytics module 175, a report generation module 180 and a maintenance module 185, hereinafter collectively referred to as management module 190, in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communication to and executed by the processing unit 130. The memory 145 further comprises a database 195. The following description explains functions of the modules when executed by the processing unit 130.

[0055] The calibration module 150 calibrates a virtual replica of the motor 105 to replicate substantially similar responses of the motor 105 in real-time, upon simulation. In other words, the virtual replica is calibrated to ensure a certain degree of fidelity with the motor 105. The virtual replica may be based on metadata associated with the motor 105, historical data associated with the motor 105 and a model of the motor 105. The metadata may include a current rating of the motor 105, a housing material of the motor 105, magnetic hysteresis coefficients of different parts of the motor 105, thermal coefficients of different parts of the motor 105, and so on. The historical data may comprise historic information related to performance, maintenance and health condition of the motor 105. The model of the motor 105 may include at least one of an artificial intelligence (AI) model and a physics-based model associated with the motor 105. In the present embodiment, the model of the motor 105 comprises a first AI model, a second AI model and a third AI model. The training of the first AI model, the second AI model and the third AI model is explained in greater detail using FIG. 2.

[0056] The virtual replica is calibrated by tuning the first AI model, the second AI model and the third AI model for accurately representing the response of the motor 105. More specifically, the virtual replica is calibrated based on deviations between a response of the virtual replica and an actual response of the motor 105. The actual response of the motor 105 corresponds to the operational data recieved from the plurality of sensing units 115. In one example, the virtual replica may be calibrated using Bayesian calibration technique. Upon calibrating, the response of the virtual replica, say at time t=10 seconds, may represent the response of the motor 105 at time t=10 seconds under the same operating conditions.

[0057] The preprocessing module 155 is configured for preprocessing of operational data received from the plurality of sensing units 115. The preprocessing of the operational data may comprise different steps for preparing the operational data for further processing. The different steps in preprocessing may include, but not limited to, data cleaning, data normalisation, data selection and so on.

[0058] The event detection module 160 is configured for detecting an event associated with a power supplied to the motor 105 based on the operational data.

[0059] The configuration module 165 configures the virtual replica of the motor 105 using the operational data, based on the event detected.

[0060] The simulation module 170 is configured for generating one or more simulation results by simulating the configured virtual replica in a simulation environment.

[0061] The analytics module 175 is configured for predicting an effect on the motor 105 due to the event based on the simulation results.

[0062] The report generation module 180 is configured for generating a notification indicating the effect on the motor 105 due to the event.

[0063] The maintenance module 185 is configured for determining a maintenance action to be performed on the motor 105 based on the effect on the motor 105 due to the event. The maintenance module 185 is further configured for scheduling the maintenance action in order to optimise a down-time associated with the electromechanical system.

[0064] Those of ordinary skilled in the art will appreciate that the hardware depicted in FIG. 1 may vary for different implementations. For example, other peripheral devices such as an optical disk drive and the like, Local Area Network (LAN)/Wide Area Network (WAN)/Wireless (e.g., Wi-Fi) adapter, graphics adapter, disk controller, input/output (I/O) adapter, network connectivity devices also may be used in addition or in place of the hardware depicted. The depicted example is provided for the purpose of explanation only and is not meant to imply architectural limitations with respect to the present disclosure.

[0065] An apparatus in accordance with an embodiment of the present disclosure includes an operating system employing a Graphical User Interface. The operating system permits multiple display windows to be presented in the Graphical User Interface simultaneously with each display window providing an interface to a different application or to a different instance of the same application. A cursor in the Graphical User Interface may be manipulated by a user through the pointing device. The position of the cursor may be changed and/or an event such as clicking a mouse button, generated to actuate a desired response.

[0066] One of various commercial operating systems, such as a version of Microsoft Windows™ may be employed if suitably modified. The operating system is modified or created in accordance with the present disclosure as described.

[0067] Embodiments of the present invention are not limited to a particular computer system platform, processing unit, operating system, or network. One or more aspects of embodiments of the present invention may be distributed among one or more computer systems, for example, servers configured to provide one or more services to one or more client computers, or to perform a complete task in a distributed system. For example, one or more aspects of embodiments of the present invention may be performed on a client-server system that comprises components distributed among one or more server systems that perform multiple functions according to various embodiments. These components comprise, for example, executable, intermediate, or interpreted code, which communicate over a network using a communication protocol. Embodiment of the present invention are not limited to be executable on any particular system or group of system, and is not limited to any particular distributed architecture, network, or communication protocol.

[0068] Disclosed embodiments provide systems and methods for management of a motor 105.

[0069] Referring to FIG. 2, in conjunction with FIG. 1, a set-up 200 for calibration of the virtual replica of the motor 105 is described, in accordance with one exemplary embodiment of the present disclosure. The set-up 200 comprises a load coupled to the shaft of the motor 105. In one example, the load may comprise a pump (not shown) and a hydraulic actuator (not shown). The load on the motor 105 is measured by the third sensing unit 115C.

[0070] The set-up 200 is used to determine empirical relationships between a voltage unbalance in the power supply and one or more parameters associated with the operation of the motor 105.

[0071] In one example, the parameter is a temperature of the stator coil corresponding to each phase of the power supply. The empirical relationship between the voltage unbalance and the temperature of the stator coil may be derived based on the temperature signature associated with the motor 105. At first, Finite Element (FE) simulations are performed to determine temperature distributions of the motor 105 corresponding to balanced voltages and unbalanced voltages. The term ‘balanced voltage’, as used herein, refers to a condition of the power supply, wherein the line voltages are equal. The term ‘unbalanced voltages’ as used herein, refers to a condition of the power supply, wherein the line voltages are unequal. For example, the unbalanced voltages may correspond to 2% undervoltage on U-phase, 3% overvoltage on W-phase and normal voltage on V-phase. In an embodiment, the FE simulation is performed based on a multiphysics model of the motor 105. Based on the FE simulations, temperature rise in different parts of the motor 105 are determined. The different parts of the motor 105 may include, stator casing, bearings, shaft, stator core, U-phase coil, V-phase coil, W-phase coil and rotor core. Each of the FE simulations corresponding to the balanced voltages and the unbalanced voltages are performed until a thermal saturation point is reached. Upon reaching the thermal saturation point, transient temperature distribution for different components of the motor 105 is generated. FIG. 3A shows a GUI 305 displaying the transient temperature distributions 310A, 310B . . . 3101 corresponding to different components part-A, part-B . . . part-I of the motor 105 for balanced voltages. FIG. 3B shows a GUI 320 displaying the transient temperature distributions 325A, 325B . . . 325I corresponding to the different components part-A, part-B . . . part-I of the motor 105 for unbalanced voltages. The different components part-A, part-B . . . part-1 may correspond to drive end bearing, non-drive end bearing, outer casing, rotor core, shaft, stator core, U-phase windings, V-phase windings and W-phase windings respectively. Further, the transient temperature distributions are validated based on the stator casing temperature obtained from the second sensing unit 115B.

[0072] Upon validation, the simulated temperatures for the different parts over a predefined interval of time are used for training the first AI model. The first AI model may be trained using machine learning techniques, including but not limited to, supervised learning techniques, unsupervised learning techniques, reinforcement learning techniques and deep learning.

[0073] The first AI model is used for predicting temperature in the different parts of the motor 105 based on the line voltages in the power supply. In the present embodiment, the first AI model may predict temperature in the stator casing, bearings, shaft, stator core, U-phase coil, V-phase coil, W-phase coil and rotor core based on the line voltages. Upon training, the first AI model is continuously calibrated based on the stator casing temperature obtained from the second sensing unit 115B. More specifically, the stator casing temperature from the second sensing unit 115B is compared with the stator casing temperature obtained from the first AI model for similar conditions of the voltage supply. Further, the first AI model may be calibrated for correcting deviations in the stator casing temperature. Consequently, deviations in predicted temperatures in other parts of the motor 105 are also corrected based on the calibration. In one example, the deviations are corrected by retraining the first AI model using stator casing temperature obtained from the second sensing unit 115B. The first AI model further determines a temperature distribution of the motor 105 during operation, based on the temperatures in the different parts. The temperature distribution of the motor 105 is further used to determine position of one or more hotspots in the phase windings. It must be understood that the temperature in a phase winding is dependent on a current density in the phase winding.

[0074] In another example, the second AI model may be trained for predicting a torque ripple profile in an output of the motor 105 based on the voltage supply. More specifically, the second AI model is trained to predict torque on a shaft of the motor 105 in an instant based on simulated values of torque at the shaft of the motor 105 for balanced voltages and unbalanced voltages. The second AI model may be trained using machine learning techniques, including but not limited to, supervised learning techniques, unsupervised learning techniques, reinforcement learning techniques and deep learning. Upon training, the second AI model is validated based on actual torque measured by the fourth sensing unit 115D. The second AI model is further calibrated to correct any deviations between the predicted torque and the actual torque. The deviations are corrected by retraining the second AI model using the actual torque measured by the fourth sensing unit 115D. Based on the torque predicted by the second AI model, a torque ripple profile of the motor 105 may be computed. The torque ripple profile may be calculated as a difference between maximum torque and minimum torque in one rotation of the shaft.

[0075] In another example, an electric circuit model of the motor 105 is used to simulate iron losses and copper losses in different parts of the motor 105. The term ‘iron loss’, as used herein, refers to energy losses in the motor 105 resulting from hysteresis and eddy currents. The term ‘copper loss’, as used herein, refers to energy losses windings of the motor 105, resulting from current flow in the windings. The electric circuit model is a physics-based model and comprises a plurality of equivalent circuits corresponding to each part of the motor 105. The equivalent circuits are connected together to represent a behaviour of the motor 105 during operation. In one example, a simplified electric circuit model of the motor 105 comprises a delta-connection, as shown in FIG. 4. FIG. 4 illustrates a GUI 400 showing the simplified electric circuit model of the motor 105, in accordance with an embodiment of the present invention. The delta-connection is further connected to three voltage inputs V1, V2 and V3, each of which correspond to the real-time line voltages on U, V and W phases, respectively, as measured by the fifth sensing unit 115E. Upon simulation, electrical and magnetic responses of the phase windings to balanced voltages and unbalanced voltages are obtained. Further, the iron losses and the copper losses in the phase windings are determined based on the electrical and magnetic responses of the phase windings. The simulated values of the iron losses and the copper losses are further used to train the third AI model for predicting iron losses and copper losses based on the voltage supply. The third AI model may be trained using machine learning techniques, including but not limited to, supervised learning techniques, unsupervised learning techniques, reinforcement learning techniques and deep learning. Upon training, the third AI model is validated based on outputs of the plurality of sensing units 115. In one example, the actual iron losses is calculated based on value of magnetic flux obtained from the first sensing unit 115A and predetermined material characteristics associated with the component, using mathematical relations known to a person skilled in the art. The predetermined material characteristics include coefficient of eddy current, volume of a material in the component, thickness of lamination and so on. The actual copper losses may be computed based on an electrical input to the motor 105, a mechanical output of the motor 105 and the actual iron losses. More specifically, the actual copper losses is a difference between the electrical input, and sum of the mechanical output and the actual iron losses. The electrical input to the motor 105 is computed based on current and voltage measured by the fifth sensing unit 115E. The mechanical output is computed based on torque measured by the fourth sensing unit 115D. In case of deviations between predicted values of iron losses and copper losses, the third AI model is retrained based on actual outputs of the sensing units 115, in order to improve accuracy of prediction. The third AI model, after training, is further used to compute iron losses and copper losses associated with different parts of the motor 105.

[0076] It must be understood by a person skilled in the art that the first AI model, the second AI model and the third AI model may be a combined to form a hybrid model that predicts empirical relationship between the voltage unbalance and a plurality of parameters associated with the motor 105. It must also be understood that each of the first AI model, the second AI model and the third AI model may be substituted by suitable metamodels for improving computation speed of the apparatus 110.

[0077] FIG. 5 shows a flowchart depicting an exemplary method 500 for managing an electromechanical system, in accordance with an embodiment of the present invention. The method 500 is implemented on the apparatus 110. Further, the method 500 comprises steps 505 to 530.

[0078] At step 505, operational data associated with the electromechanical system is received from one or more sensing units in real-time.

[0079] At step 510, an event associated with a power supplied to the electromechanical system is detected based on the operational data.

[0080] At step 515, a virtual replica of the electromechanical system is configured using the operational data, based on the event detected.

[0081] At step 520, one or more simulation results are generated by simulating the configured virtual replica in a simulation environment.

[0082] At step 525, an effect on the electromechanical system due to the event is predicted based on the simulation results.

[0083] At step 530, a notification indicating the effect on the electromechanical system due to the event is generated.

[0084] The steps 505-530 in the method 500 is explained in greater detail for management of the motor 105, by considering the event to be a voltage unbalance in the power supplied to the motor 105.

[0085] FIG. 6 shows a flowchart depicting an exemplary method 600 for detecting the event associated with the power supplied to the motor 105 based on the operational data, in accordance with an embodiment of the present invention. The operational data received from the plurality of sensing units 115 is firstly preprocessed upon execution of the preprocessing module 155 by the processing unit 130. Upon preprocessing, the event detection module 160 is executed to perform steps 605 and 610 for detecting the unbalanced voltages.

[0086] At step 605, values of the parameters in the operational data is provided as input to at least one correlation model. The correlation model correlates the parameters to one or more events in the power supply. In one implementation, the correlation model relates a stray magnetic flux measured by the first sensing unit 115A to deviations in line voltages of the power supply from an average of the line voltages.

[0087] At step 610, the voltage deviations from the correlation model is compared to a predefined range or predefined value. Based on the comparison, the event is detected. For example, if the voltage deviation in any of the line voltages is greater than 0.5%, a voltage unbalance is detected.

[0088] Upon detecting the voltage unbalance an effect of the unbalance on the motor 105 is predicted. In one embodiment, the effect may be associated with a performance of the motor 105 as described using FIG. 7. In another embodiment, the effect may be associated with damage to a component of the motor 105 as described using FIG. 8A.

[0089] FIG. 7 shows a flowchart depicting a method 700 for predicting effect of the voltage unbalance on a performance of the motor 105, in accordance with an embodiment of the present invention. More specifically, the method 700 relates to detecting presence of torque ripples in the output of the motor 105 upon detecting the voltage unbalance in the power supply. The method comprises steps 705 to 720.

[0090] At step 705, the configuration module 165 updates the virtual replica using the operational data in real-time. Upon updating, the virtual replica represents a state of the motor 105 in real-time.

[0091] At step 710, the simulation module 170 executes simulation instances of a simulation model of the motor 105 based on the updated virtual replica. The simulation instance is executed in the form of stochastic simulations. Subsequently, simulation results are generated from the stochastic simulations. Similarly, a plurality of simulation instances are executed based on the different instances of the configured virtual replica. In the present embodiment, the simulation results are indicative of a torque on the shaft of the motor 105 at different instances of time. In one example, the different instances of time may be associated with a complete rotation of the shaft of the motor 105.

[0092] At step 715, the analytics module 175 analyses the behaviour of the motor 105 based on the simulation results corresponding to the plurality of simulation instances. In one example, the behaviour is analysed using transient analysis. Based on the analysis, maximum torque and minimum torque measured during the complete rotation of the shaft are determined. Further, the torque ripple is detected if the difference between the maximum torque and the minimum torque is greater than a predefined value, for example, 0.1 Nm. Further, the report generation module 180 generates a notification indicating the presence of torque ripples as shown in 720, on the GUI 140.

[0093] FIG. 8A shows a flowchart depicting an exemplary method 800 for predicting damage to components of the motor 105 in real-time, in accordance with an embodiment of the present invention. More specifically, the damage to the component resulting from formation of hotspots is predicted, upon detecting the voltage unbalance in the power supply. The method 800 comprises steps 805 to 825.

[0094] At step 805, the configuration module 165 updates the virtual replica using the operational data in real-time. Upon updating, the virtual replica represents a state of the motor 105 in real-time.

[0095] At step 810, the simulation module 170 executes simulation instances of a simulation model of the motor 105 based on the updated virtual replica. The simulation instance is executed in the form of stochastic simulations. Subsequently, simulation results are generated from the stochastic simulations. Similarly, a plurality of simulation instances are executed based on the different instances of the configured virtual replica. The simulation results are indicative of a temperature distribution of the motor 105 at different instances of time.

[0096] At step 815, the analytics module 175 analyses the behaviour of the motor 105 based on the simulation results corresponding to the plurality of simulation instances. In the present embodiment, the behaviour is analysed using transient analysis, for computing the thermal profile associated with the components of the motor 105 based on the temperature distribution corresponding to each of the instances. The transient analysis may be performed using visual ML. Further, hotspots are identified based on the thermal profile associated with the components. For example, if the thermal profile of the U-phase winding indicates a temperature greater than 40 degrees for a period of more than 5 minutes, a hotspot is detected on the U-phase winding.

[0097] At step 820, the analytics module 175 predicts damage to the component based on the presence of the hotspot. The damage may be a thermal fatigue associated with the U-phase winding resulting from the hotspot. In the present example, the damage to the U-phase winding is estimated using, for example, Arrhenius equation. The Arrhenius equation is used to compute rate of degradation of a winding insulation of the U-phase winding based on the thermal profile.

[0098] At step 825, the report generation module 180 generates a notification indicating the damage to the U-phase winding, on the GUI 140. For example, the damage may be thermal stress in different parts of the U-phase winding. Further, varying levels of thermal stress in different portions of the U-phase winding may be indicated using different shades of colours. In one example, bright red may indicate a high thermal stress and light red may indicate relatively lower thermal stress. The notification is further displayed on the Graphical User Interface 140.

[0099] In a further embodiment, the analytics module 175 determines remaining useful life of the motor 105 based on the damage predicted for the component. The remaining useful life of the motor 105 is computed by a predetermined mathematical model based on a fatigue life of the component and the rate of degradation of the winding insulation. The fatigue life is computed using Coffin-Manson relation. Similarly, fatigue life of all the components in the motor 105 is determined by predicting damage to the components from the voltage unbalance. The remaining useful life of the motor 105 may correspond to the least value of fatigue life of all the components.

[0100] FIG. 8B shows a flowchart depicting a method 850 for generating recommendations for optimizing energy consumption of the motor 105, in accordance with an embodiment of the present invention. The method 900 comprises steps 855 to 880, which follow step 815 of FIG. 8A.

[0101] At step 855, the analytics module 175 computes copper losses and iron losses in each of the stator and the rotor of the motor 105. The copper losses and iron losses are computed by the third AI model, based on the thermal profile associated with the components.

[0102] At step 860, the analytics module 175 determines an additional energy consumed by the motor 105. For example, an electrical input P.sub.in to the motor 105 is is calculated based on current and voltage values associated with the power supply, as measured by the fifth sensing unit 115E. Further, stator losses P.sub.s is computed as a sum of the iron losses and the copper losses in the stator. The stator losses are further used to determine a electrical input to the rotor P.sub.2 is calculated as:

P.sub.2=P.sub.in−P.sub.s  (1)

[0103] Further, a mechanical output P.sub.m of the motor 105 is calculated based on the electrical input to the rotor P.sub.2 and the copper losses P.sub.R in the rotor as:

P.sub.m=P.sub.2−P.sub.R  (2)

[0104] At step 865, the analytics module 175 computes an efficiency, η.sub.1 of the motor 105 based on the mechanical output P.sub.m and the electrical input P.sub.in.

η.sub.1=P.sub.m/P.sub.in  (3)

[0105] At step 870, the analytics module 175 compares the efficiency of the motor 105 to a predetermined efficiency of the motor 105 corresponding to balanced voltages, under similar load conditions.

[0106] At step 875, a difference in the efficiencies of the motor 105 corresponding to unbalanced voltage and balanced voltage is determined based on the comparison. Further, an optimal load of the motor 105 corresponding to the unbalanced voltages, is determined based on the difference in the efficiencies. For example, the optimal load is determined, using equations (1) to (3) such that the efficiency of the motor 105 corresponding to the unbalanced voltages is greater than or equal to the predetermined efficiency of the motor 105. Further, one or more recommendations are generated to adjust the load on the motor 105 to the optimal load or a lesser load as shown in step 880. In one example, the one or more recommendations may include predefined instructions for reducing the load without affecting the motor 105.

[0107] In one example, cost savings from performing load adjustments on the motor 105 may also be shown along with the recommendations for the load ajustment. The cost savings may be computed for a specific period of time based on a load factor of the motor 105, a rating of the motor 105, the difference in efficiencies for the balanced voltage and the unbalanced voltage for a given period of time. For example, the cost savings may be calculated based on difference in energy consumption of the motor 195 corresponding to both balanced voltages and unbalanced voltages. If the power rating of the motor 105 is 100 kW and the motor 105 is operating at 75% of full load, the mechanical power developed by the motor 105 is roughly 75 kW. Let the predetermined efficiency η.sub.2 of the motor 105 corresponding to balanced voltages be 95.2% and the efficiency η.sub.1 corresponding to voltage unbalance of 2.5% be 93.9%. Assume that the cost of energy is $0.08 per kWh and the load factor is 1. The cost savings for 8000 operational hours is calculated as:

[00001]$\begin{array}{c}\mathrm{Cost}\mathrm{savings}=\mathrm{Mechanical}\mathrm{power}\mathrm{developed}\times \mathrm{Operational}\mathrm{hours}\times \\ \mathrm{Load}\mathrm{factor}\times 100\left(1/{\eta }_1-1/{\eta }_2\right)\times \\ \mathrm{cost}\mathrm{per}\mathrm{energy}\mathrm{unit}\\ =75\mathrm{kW}\times 8000h\times 1\times 100\left(1/93.9-1/95.2\right)\times 0.08\\ =\mathrm{\$698}\end{array}$

[0108] The report generation module 180 may further generate a notification indicating the recommendations for load adjustment and the cost savings on the GUI 140.

[0109] FIG. 9 shows a flowchart depicting an exemplary method 900 for determining a maintenance action on the motor 105 based on the effect on the motor 105 from the event, in accordance with an embodiment of the present invention. The method 900 comprises 905-915 which are performed upon execution of the maintenance module 185.

[0110] At step 905, a keyword corresponding to a damaged component and type of damage associated with the damaged component, is identified based on predefined rules. In the example of FIG. 8A, the keyword corresponding to the damaged component may be ‘U-phase winding’ and the keyword corresponding to the type of damage may be ‘hotspot’. In one implementation, the keywords may be stored in the database 195. Upon determining damage to the component, the keyword corresponding to the component is identified from the database 195.

[0111] At step 910, the keyword corresponding to the component is used for semantics based searching, for example, on a knowledge graph. The knowledge graph comprises linked data corresponding to various aspects of the motor 105. For example, the linked data may comprise information on the different parts of the motor 105, types of damages associated with each of the components and corresponding maintenance actions.

[0112] At step 915, the maintenance action is identified from the linked list, corresponding to the damaged component and the type of damage to the component. For example, in case of damage to the U-phase winding due to formation of hotspots, the maintenance action may include rewinding of the U-phase winding.

[0113] The apparatus 110 schedules the identified maintenance action to optimise downtime of the motor 105. In one example, the maintenance action is scheduled before the remaining useful life of the motor 105 expires. In one example, the maintenance action may be scheduled before 30 working hours from the expiry of the remaining useful life.

[0114] Embodiments of the present invention may take the form of a computer program product comprising program modules accessible from computer-usable or computer-readable medium storing program code for use by or in connection with one or more computers, processors, or instruction execution system. For the purpose of this description, a computer-usable or computer-readable medium is any apparatus that may contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium may be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation mediums in and of themselves as signal carriers are not included in the definition of physical computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, random access memory (RAM), a read only memory (ROM), a rigid magnetic disk and optical disk such as compact disk read-only memory (CD-ROM), compact disk read/write, and DVD. Both processors and program code for implementing each aspect of the technology may be centralized or distributed (or a combination thereof) as known to those skilled in the art.

[0115] Although the present invention has been disclosed in the form of embodiments and variations thereon, it will be understood that numerous additional modifications and variations could be made thereto without departing from the scope of the invention.

[0116] For the sake of clarity, it is to be understood that the use of “a” or “an” throughout this application does not exclude a plurality, and “comprising” does not exclude other steps or elements.