Description: A SYSTEM IMPLEMENTING DIGITAL TWIN OF A MOTOR FOR CONDITION MONITORING

FIELD OF THE INVENTION

[0001] The present subject matter relates to a system and a method thereof for predicting live temperature of a stator and a rotor of traction motors or any induction motor using the principle of fluid dynamics, artificial neural networks (ANN) and deep learning (DL) technique. In particular, the present subject matter relates to the system that is basically a digital twin of the motor under implementation and can be used to monitor the temperatures of the stator and the rotor of traction motors or any induction motors to prevent insulation failures.

BACKGROUND OF THE INVENTION

[0002] The background represents, information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referred is prior art.

[0003] The thermal management of motors is very crucial while running on critical applications like tractions, e-vehicles etc., because electrical insulation of motor windings has a temperature limit and the temperature of the motor affects its efficiency. In this process, every effort is being put forward to improve the efficiency of electric motors and accordingly, new designs are coming into the market. Every new motor design needs to be analyzed for the flow and thermal profiles using Fluid Dynamics or any physics based models, before they come into for commercial usage. Similarly, it would be important to monitor real-time flow and thermal profiles when the motor is in working condition, to be able to assess health of the motor enabling responsiveness to take appropriate proactive actions in time. However, conventional methods of monitoring of temperature by RTD’s might not be feasible as providing sensors inside the motor is a difficult proposition, especially in rotating parts such as rotor core and rotor windings.

[0004] In view of the above discussion, it is observed that there is a need to develop a system that can monitor live rotor and stator temperatures across the motor.
PRIOR ARTS OF THE INVENTION

[0005] There are some systems/methods known in the art regarding condition monitoring of motors. These are discussed herein below:

[0006] In US7769552B2 titled “Method and apparatus for estimating induction motor rotor temperature” where the invention discloses a method and an apparatus to provide continuous and reliable rotor temperature estimates for line-connected induction motors during steady-state and/or dynamic motor operations. Herein, rotor temperature is calculated from voltage and current measurements without any temperature or speed sensors. First, complex space vectors are synthesized from voltage and current measurements. Second, the instantaneous rotor speed is detected by calculating the rotational speed of a single rotor slot harmonic component with respect to the rotational speed of the fundamental frequency component. Third, the positive sequence fundamental frequency components are extracted from complex space vectors. Fourth, the rotor time constant is estimated in a model-reference adaptive system based on a dynamic induction motor equivalent circuit model. Finally, the rotor temperature is calculated according to the linear relationship between the rotor temperature and the estimated rotor time constant. Real-time induction motor thermal protection is achieved through this continuous tracking of the rotor temperature.

[0007] The invention US8487575B2 titled “Electric motor stator winding temperature estimation” is on a temperature estimation controller and methods are provided for estimating stator winding temperature over a full range of motor operating speeds. In one implementation, the angular velocity of a motor is determined along with a total power loss for each phase of said motor. The total power loss in each phase comprises stator winding power loss and a core power loss. Stator winding temperatures for each phase of motor can then be estimated based on the total power loss in that phase, and a combined thermal impedance for that phase. The combined thermal impedance comprises a first thermal impedance between the stator winding and the stator core, and a second thermal impedance between the stator core and the motor coolant.

[0008] In CN110323994A titled “Method and system for estimating rotor temperature of motor online in real time, vehicle and computer readable storage medium” discloses a method, a system, a vehicle and computer readable storage mediums of a kind of real-time online estimation motor rotor temperature, first determine whether system powers on, then judge motor instantaneous operating conditions according to motor actual speed, torque. Secondly motor rotor temperature value is estimated according to motor operating state, next judges that the decision of motor rotor temperature correction conditions is updated its value, to eliminate score accumulation error. Finally electric condition carries out non-loss property storage to revised temperature of rotor under judgment system, and rotor initial temperature can be estimated after powering on again so as to system. The present invention improves the accuracy that motor is estimated in complicated operating condition lower rotor part temperature online.

[0009] In CN102156000B titled “Electric motor, electric motor winding temperature detection method and device as well as electric motor winding thermal protection method and device” discloses an electric motor, an electric motor winding temperature detection method and a device as well as an electric motor winding thermal protection method and device. The electric motor winding temperature detection method comprises the following steps: acquiring the electric motor winding temperature at the previous moment; then calculating temperature rise of a rotor equivalent body after operating for one time step; and finally calculating the electric motor winding temperature at the next moment by superimposing the electric motor winding temperature at the previous moment with the temperature rise of the rotor equivalent body after operating for one time step. According to the electric motor winding temperature detection method, the electric motor winding temperature is monitored in real time; the performance of the electric motor is exerted; and the sensitivity of the electric motor is improved.

[0010] In CN105181173A titled “Method and apparatus for monitoring temperature rise of motor winding”, there is disclosed a method and an apparatus for monitoring a temperature rise of a motor winding. The method comprises the following steps: establishing a dynamic thermal energy equation of a motor; acquiring, calculating and obtaining state parameter values of the motor, related to the dynamic thermal energy equation at a current time point; and based on the dynamic thermal energy equation, according to the state parameter values, obtaining the temperature rise of the winding of the motor at the current time point through calculation.

[0011] However, the apparatus and the corresponding methods discussed in the prior arts above are either complicated or involves indirect measurement of the parameters of the motor. Moreover, some of this techniques involves change of an internal arrangement of the motor thereby making the process more complex. Further, as these techniques are complex and involves disassembly of the motor to some extent, these are relatively expensive to implement.

[0012] Towards this direction, the present disclosure proposes a system and a method that intends to alleviate the above mentioned limitations and is user friendly as well.

OBJECTS OF THE INVENTION

[0013] It is an object of the present subject matter to overcome the aforementioned and other drawbacks existing in the prior art systems.

[0014] It is a principal object of the present subject matter to propose a system and a corresponding method for live monitoring of stator and rotor temperatures in a motor without requiring to change an internal structure of the motor.

[0015] It is another significant object of the present subject matter to develop a system that is a digital twin of the motor to be monitored.

[0016] It is yet another object of the present subject matter to design a system using the principles of artificial neural network and deep learning techniques.

[0017] It is another object of the present subject matter to develop a system and a method that can be implemented in a cost effective manner.

[0018] It is another object of the present subject matter to develop a system and a method that is simple and easy to implement.

[0019] These and other objects and advantages of the present subject matter will be apparent to a person skilled in the art after consideration of the following detailed description taking into consideration with accompanied drawings in which preferred embodiments of the present subject matter are illustrated.

SUMMARY OF THE INVENTION

[0020] The present invention relates to a system which is basically a digital twin of a traction motor or an induction motor or any other motor. According to the present disclosure, the system is designed in order to monitor live temperatures of stator and rotor of the motor.
[0021] According to an embodiment of the present disclosure, there is provided a system implementing a digital twin of an electric motor for health monitoring of the electric motor. In an aspect, the system comprises of an input device configured for receiving operating inputs from a motor, a data processing device configured to process data received from the input device, an artificial intelligence device configured to monitor live temperatures of a rotor and stator of the electric motor by predicting temperature of the rotor and the stator with respect to a multitude of load parameters. The artificial intelligence device is developed based on the data generated from a data generation device for various load conditions of the motor from which datasets are segregated to train and test within the artificial intelligence device. Further, it is validated with test/ data generated from the data generation device. Based on the accuracy and performance of the artificial intelligence device, this is deployed in the system to monitor the live temperatures of the motor for any operating conditions.
[0022] In an aspect, in the system, the inputs include temperature, pressure, velocity, density in X, Y and Z coordinates respectively, across a multitude of points in the electric motor, are exported from the data generation device and is given as an input to the artificial intelligence device.
[0023] In an aspect, in the system, the artificial intelligence device operates using a principle of convolutional neural network and deep learning, respectively.
[0024] In an aspect, in the system, the training dataset and the testing dataset, respectively, of the artificial intelligence device includes 65 percent to 70 percent and 35 percent to 30 percent of the dataset received from the data processing device.
[0025] In an aspect, in the system, the multitude of load parameters include percentage load of the motor (25%, 50%, 75%, 100%, 125%) and heat losses pertaining to the load
[0026] In an aspect, there is provided a method for health monitoring of an electric motor implemented using a system. The method comprises receiving input dataset and fetching them to an input device, collecting and processing data by a data processing device, exporting processed data to an artificial intelligence device which is based on data obtained from a data generation device, preferably by CFD simulations. Herein, temperature predicted across a cross section of one plane of the electric motor is mapped throughout all cross sections of the electric motor using multiple neural networks in between by the artificial intelligence device.

[0027] To further understand the characteristics and technical contents of the present subject matter, a description relating thereto will be made with reference to the accompanying drawings. However, the drawings are illustrative only but not used to limit the scope of the present subject matter.
[0028] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING(S)

[0029] It is to be noted, however, that the appended drawings illustrate only typical embodiments of the present subject matter and are therefore not to be considered for limiting of its scope, for the invention may admit to other equally effective embodiments. The detailed description is described with reference to the accompanying figures. In the figures, a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system or methods or structure in accordance with embodiments of the present subject matter are now described, by way of example, and with reference to the accompanying figures, in which

[0030] Figure 1 illustrates shows a schematic of an electric motor with its parts and air flow path in accordance with an embodiment of the present disclosure;

[0031] Figure 2 illustrates an exemplary block diagram of a system implementing digital twin of the electric motor in accordance with an exemplary embodiment of the present disclosure;

[0032] Figure 3 shows the planes required for mapping of temperature by the system in accordance with an embodiment of the present disclosure;

[0033] Figure 4 shows an exemplary plane to plane temperature mapping as implemented by the system in accordance with an exemplary embodiment of the present disclosure;

[0034] Figure 5 shows an exemplary block diagram for training and deployment of the dataset in the artificial intelligence device for the system in accordance with an exemplary embodiment of the present disclosure;

[0035] Figure 6 depicts an exemplary flow diagram of operation of the system implementing Digital Twin creation of the motor using data obtained from the data generation device and the artificial intelligence device in accordance with an exemplary embodiment of the present disclosure;

[0036] Figure 7 shows a temperature contour predicted using fluid dynamics data generated by a data generation device along a cross-sectional plane of the motor in accordance with an embodiment of the present disclosure;

[0037] Figure 8 shows the temperature contour of a rotor in accordance with an embodiment of the present disclosure;

[0038] Figure 9 shows the temperature contour of a stator in accordance with an embodiment of the present disclosure; and

[0039] Figure 10 shows various planes created along Y-direction to capture the data of the motor generated by the data generation device following a principle of Fluid Dynamics by the system in accordance with an embodiment of the present disclosure; and

[0040] Figure 11 shows the comparison of temperature plot from data that are generated from data generation device of the electric motor and that trained by the artificial intelligence device in accordance with an embodiment of the present disclosure.

[0041] The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.

DETAILED DESCRIPTION OF INVENTION WITH REFERENCE TO THE DRAWINGS OF THE PREFERRED EMBODIMENTS

[0042] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0043] While the embodiments of the disclosure are subject to various modifications and alternative forms, specific embodiment thereof have been shown by way of example in the figures and will be described below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.

[0044] The terms “comprises”, “comprising”, or any other variations thereof used in the disclosure, are intended to cover a non-exclusive inclusion, such that a device, system, assembly that comprises a list of components does not include only those components but may include other components not expressly listed or inherent to such system, or assembly, or device. In other words, one or more elements in a system or device proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or device.

[0045] Typically, representation of physical systems into digital (cyber) domain is possible by using first principle methods (including basic mathematical equations or advanced tools) or through data-driven methods like Computational Intelligence (CI) (also known as Artificial Intelligence (AI), Machine Learning (ML), Machine Intelligence (MI), Deep Learning (DL), Soft Computing etc.). The basic limitation of the first principle based approach for detailed represented models is the difficulty to simulate in real-time. On the other hand, the major advantage of using CI or DL approach over the first principle approach is, CI approach can simultaneously work at par with real-world physical systems due to its simplicity of formulation. Further, in CI approach it is possible to continuously improve the understanding of the given system from time to time, based on the new observations (i.e. evidence in the form of data) collected in terms of its behavior. On the contrary, using the first principle approach, accommodating this factor would be very cumbersome and sometimes, it may lead to whole new efforts altogether. The present invention is an effort to implement a digital twin for a traction motor, to be able to monitor flow and temperature distribution in the various cross-sections of the motor.

[0046] Figure 1 shows a schematic of an electric motor (100) along with its parts and a path of air flow in accordance with an embodiment of the present disclosure. The cross-sectional view shows various parts of the motor along with air flow path. Air Flow path is shown in orange color.

[0047] In an aspect, as can be seen from Figure 1, the electric motor (100) comprises of a rotor shaft (101), a fan (102), a rotor core (103), a rotor winding (104), a stator core (105), a stator winding (106), a plurality of rotor sub-slots (107), a rotor stator air gap (108) and a plurality of stator slots (109). Herein, the rotor winding (104), the stator winding (106) and the stator core (105) are the sources for heat generation. This heat is dissipated by flow of air through the air flow path in the electric motor (100).

[0048] Figure 2 depicts an exemplary system (200) capable of obtaining a digital twin of the electric motor as described in Figure 1 in accordance with an exemplary embodiment of the present disclosure. In an aspect, the system (200) can be a single board computer built on a single circuit board, with microprocessor(s), memory, input/output (I/O) interface, and other features required for operation of the system (200).

[0049] In an aspect, the system (200) may comprise of an input device (205), a data generation device (206), a data processing device (207) and an artificial intelligence device (208).
[0050] In an aspect, the input device (205) receives data from the operating parameters of motor (200). The inputs may be a measure of current, voltage, power while measuring or predicting in real-time rotor and stator temperatures. Whereas, to develop an ANN model, the input parameters are temperature, pressure, velocity, density in X, Y and Z coordinates respectively, across various points of the motor. An exemplary data set is provided in Table 1 for better understanding.
[0051] In an aspect, the data processing device (207) obtains data from the input device (205) and processes them. These data sets of temperature, pressure, velocity, density in X, Y and Z coordinates are generated. The data processing device (207) transforms the data in appropriate format that is suitable for handling by the artificial intelligence device (208).
[0052] In an aspect, the artificial intelligence device (208) is developed by training and testing the model in the artificial intelligence device (208) using fluid dynamic dataset obtained from a data generation device (206). These are created using the principle of convolutional neural network (CNN) and deep learning (DL) for prediction of temperatures of the electric motor (100) with respect to the load parameters.
[0053] In an aspect, the training dataset is fetched with the data obtained from the data processing device (207) in order to train the system with suitable inputs. Further, the testing data while the system is in operation and obtains a degree of matching between the trained data and the test data located in a memory of the system. The artificial intelligence device (208) fine tunes the degree of matching between the training and the testing dataset before fetching the data to a validation device (209).
[0054] In an aspect, the validation device (209) validates the results fetched by the artificial intelligence device (208). Herein, the system (200) represents a digital twin of the electric motor and displays the values of temperature and pressure across various points of the electric motor operating in real time environment. The results may be displayed on a graphical user interface thereby enabling an operator to efficiently monitor health of the motor and take necessary actions if required.
[0055] The various illustrative logical blocks, modules, units and circuits described in connection with the present disclosure may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general- purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. A hardware processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. If implemented in hardware, an example hardware configuration may comprise a processing system in a physical or wireless node. Hardware units store technical instructions, data or any combination thereof that, when executed by an apparatus such as a processor which alone or in combination with other hardware components, cause the processing system to perform various technical functions.

Table 1. Values of different parameters across various points of the motor

Point name X-coordinate Y-coordinate Z-coordinate Temperature Pressure Velocity Density
Point 16552 0.080735 m -0.28 m -0.114376 m 314.016 K 101292 kg m^-1 s^-2 25.0967 m s^-1 1.185 kg m^-3
Point 16553 0.080735 m -0.3 m -0.114376 m 312.49 K 101295 kg m^-1 s^-2 24.9255 m s^-1 1.185 kg m^-3
Point 16554 0.080735 m -0.32 m -0.114376 m 310.546 K 101295 kg m^-1 s^-2 7.22623 m s^-1 1.185 kg m^-3
Point 16555 0.080735 m -0.34 m -0.114376 m 310.549 K 101294 kg m^-1 s^-2 7.18353 m s^-1 1.185 kg m^-3
Point 16556 0.080735 m -0.36 m -0.114376 m 310.603 K 101293 kg m^-1 s^-2 6.84173 m s^-1 1.185 kg m^-3
Point 16557 0.080735 m -0.38 m -0.114376 m 310.846 K 101293 kg m^-1 s^-2 6.1715 m s^-1 1.185 kg m^-3
Point 16558 0.080735 m -0.4 m -0.114376 m 311.855 K 101294 kg m^-1 s^-2 3.82172 m s^-1 1.185 kg m^-3
Point 16559 0.080735 m -0.42 m -0.114376 m 312.011 K 101294 kg m^-1 s^-2 4.27595 m s^-1 1.185 kg m^-3
Point 16560 0.080735 m -0.44 m -0.114376 m 313.472 K 0 0 7800 kg m^-3
Point 16561 0.0644089 m 0.46 m -0.124304 m 324.041 K 0 0 7800 kg m^-3
Point 16562 0.0644089 m 0.44 m -0.124304 m 322.6 K 100875 kg m^-1 s^-2 3.06301 m s^-1 1.185 kg m^-3
Point 16563 0.0644089 m 0.42 m -0.124304 m 0 0 0 0
Point 16564 0.0644089 m 0.4 m -0.124304 m 319.447 K 101148 kg m^-1 s^-2 16.491 m s^-1 1.185 kg m^-3
Point 16565 0.0644089 m 0.38 m -0.124304 m 324.547 K 101142 kg m^-1 s^-2 18.2022 m s^-1 1.185 kg m^-3
Point 16566 0.0644089 m 0.36 m -0.124304 m 325.592 K 101139 kg m^-1 s^-2 16.8553 m s^-1 1.185 kg m^-3
Point 16567 0.0644089 m 0.34 m -0.124304 m 324.483 K 101135 kg m^-1 s^-2 16.0017 m s^-1 1.185 kg m^-3
Point 16568 0.0644089 m 0.32 m -0.124304 m 322.196 K 101133 kg m^-1 s^-2 16.0263 m s^-1 1.185 kg m^-3

[0056] Figure 3 and Figure 4 shows respective planes and the plane to plane temperature mapping that has been designed by the artificial intelligence device (208). The data from one plane to another plane is mapped for prediction of temperatures in between the planes using the artificial intelligence device (208).
[0057] In an aspect, the temperature of a first plane data is predicted following the principle of CNN in line with the real time data i.e., power, load, etc. Then it is mapped to a next plane for predicting temperature of that plane.
[0058] In an aspect, similarly, temperatures predicted across a cross section of one plane of the electric motor (100) is mapped throughout all cross sections of the electric motor (100) using multiple neural networks in between by the artificial intelligence device (208). The temperature mapping is done from the already generated Fluid Dynamics data by the data generation device (206) and deployed by the artificial intelligence device (208) for prediction of temperatures at any other intermediary locations.
[0059] Figure 5 shows the block diagram for training and deployment of the artificial intelligence device (208) for the system (200) in order to achieve the digital twin of the electric motor. The data generated for various operation conditions of the motor are imported to the artificial intelligence device (208) for the purpose of training. Herein, 70% of the data is used for as the training dataset and 30% of the data is used as testing dataset. The validation of the results generated by the artificial intelligence device (208) is performed by the validation device (209). The corresponding flow of process is shown in the block diagram.

[0060] Figure 6 depicts an exemplary flow diagram of operation of the system implementing Digital Twin creation of the motor using in accordance with an exemplary embodiment of the present disclosure. The order in which the method (700) is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method, or an alternative method. Furthermore, method may be implemented by processing device(s) or computing device(s) through any suitable hardware.

[0061] At block (701), the method includes designing inputs for the Design of Experiment (DoE) matrix and fetching them to the input device (205).

[0062] At block (702), the method includes analyzing CHT for different cases by the input device (205) after receiving data generated by the principle of Fluid Dynamics.

[0063] At block (703), the method includes collecting data and exporting them by the data processing device (207) to the artificial intelligence device (208).

[0064] At block (704), the method includes preparation of the training and testing dataset by the artificial intelligence device (208).

[0065] At block (705), the method includes fine tuning of the results by the artificial intelligence device (208) by optimizing the training and the testing data set.

[0066] At block (706), the method includes validation of the resulting parameters as received from the artificial intelligence device (208) by a validation device (209). Herein, when the electric motor (100) is operated in real time, the system (200) deploys the digital twin of the electric motor (100) in real time as well. Further, the system (200) predict an estimate of temperature of the rotor and the stator and also the temperature and pressure distribution across various cross sections of the electric motor (100).

[0067] The results from the data generation device (206) are depicted with reference to Figure 7 to Figure 10. Figure 7 shows a temperature contour predicted from the data generation device (206) using a principle of Fluid Dynamics along a cross-sectional plane of the motor. According to the present invention, the temperatures along the various cross-sections of the motor are predicted for various operating parameters such as load, ambient temperature, etc. The contour depicts the temperature of the solid parts and the air flow path along that cross-sectional plane.

[0068] Figure 8 shows the temperature contour of rotor including the rotor core (103), rotor windings (104) and rotor shaft (101). In real time operation of the motor, rotor temperatures cannot be measured or predicted using Resistance Temperature Detectors (RTD’s) or any other equipment, as it is a rotating part. According to the present invention, the rotor temperatures are predicted for any operating conditions of the motor using the artificial; intelligence device (208) and data generated by the data generation device (206).

[0069] Figure 9 shows the temperature contour of the stator including the stator core (105) and stator winding (106). According to the present invention, the stator temperatures are predicted for any operating conditions of the motor using artificial intelligence device (208) and data generated by the data generation device (206) through Fluid Dynamics.

[0070] Figure 10 shows various planes created along Y-direction to capture the data generated of the motor. This data is exported in the form of point data in format. An example of the corresponding dataset is shown in Table 1 in previous section.

[0071] Test Results

Figure 11 shows the comparison of temperature plot from data that are generated from data generation device (206) of the electric motor (100) and that trained by the artificial intelligence device (208). According to the present invention, the figure depicts testing of dataset with the fluid dynamic data generated by the data generation device (206) as performed using the artificial intelligence device (208). The comparison of outputs from the artificial intelligence device (208) are in good agreement with the contours of the data generated. Based on these results, the temperatures at any cross-section and any location can be predicted for any operating condition of the motor.

[0072] Advantages of the invention
All in all, the system (200) described in the present disclosure is having the following advantages:
a) The system (200) facilitates efficient health monitoring of the electric motor without involving dismantling of any actual components of the motor
b) The system (200) is easy to implement
c) The system (200) provides real time status of the motor
d) The system (200) helps in avoiding insulation failures of the motor by predicting the temperatures in advance
e) The system (200) is open to continuous upgradation depending on the requirements
f) The system (200) is cost effective

[0073] In this context, it is to be mentioned that the system as disclosed in the present subject matter can be implemented for condition motoring of any electric motors including motors used in electric vehicles by creating appropriate digital twin for predicting and prescribing proper actions thereby avoiding catastrophic failures.

[0074] It should be noted that the description and figures merely illustrate the principles of the present subject matter. It should be appreciated by those skilled in the art that conception and specific embodiment disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present subject matter. It should also be appreciated by those skilled in the art that by devising various systems that, although not explicitly described or shown herein, embody the principles of the present subject matter and are included within its spirit and scope. Furthermore, all examples recited herein are principally intended expressly to be for pedagogical purposes to aid the reader in understanding the principles of the present subject matter and the concepts contributed by the inventor(s) to furthering the art and are to be construed as being without limitation to such specifically recited examples and conditions. The novel features which are believed to be characteristic of the present subject matter, both as to its organization and method of operation, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures.

[0075] Although embodiments for the present subject matter have been described in language specific to package features, it is to be understood that the present subject matter is not necessarily limited to the specific features described. Rather, the specific features and methods are disclosed as embodiments for the present subject matter. Numerous modifications and adaptations of the system/device of the present invention will be apparent to those skilled in the art, and thus it is intended by the appended claims to cover all such modifications and adaptations which fall within the scope of the present subject matter.

[0076] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).

[0077] In those instances, where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

[0078] It will be further appreciated that functions or structures of a plurality of components or steps may be combined into a single component or step, or the functions or structures of one-step or component may be split among plural steps or components. The present invention contemplates all of these combinations. Unless stated otherwise, dimensions and geometries of the various structures depicted herein are not intended to be restrictive of the invention, and other dimensions or geometries are possible. In addition, while a feature of the present invention may have been described in the context of only one of the illustrated embodiments, such feature may be combined with one or more other features of other embodiments, for any given application. It will also be appreciated from the above that the fabrication of the unique structures herein and the operation thereof also constitute methods in accordance with the present invention. The present invention also encompasses intermediate and end products resulting from the practice of the methods herein. The use of “comprising” or “including” also contemplates embodiments that “consist essentially of” or “consist of” the recited feature.

, C , Claims: We Claim:
1. A system (200) implementing a digital twin of an electric motor (100) for health monitoring of the electric motor (100), the system (200) comprising:
- an input device (205) configured for receiving design inputs for formulation of a design of experiment matrix;
- a data processing device (207) configured to process data received from the input device (205);
- an artificial intelligence device (208) configured to develop a training dataset and a testing dataset upon receiving processed data from the data processing device (207) to determine stator and rotor temperatures;
- a validation device (209) configured to validate the training and the testing dataset developed by the artificial intelligence device (208) to operate the system (200),
- wherein the system (200) monitors live temperature of a rotor and a stator of the electric motor (100) by predicting temperature of the rotor and the stator with respect to a multitude of load parameters.
2. The system (200) as claimed in claim 1, wherein the design inputs include temperature, pressure, velocity, density in X, Y and Z coordinates respectively, across a multitude of points in the electric motor (100).
3. The system (200) as claimed in claim 1, wherein the artificial intelligence device (208) operates using a principle of convolutional neural network and deep learning, respectively.
4. The system (200) as claimed in claim 1, wherein the training dataset and the testing dataset, respectively, of the artificial intelligence device (208) includes 65 percent to 70 percent and 35 percent to 30 percent of the dataset received from the data processing device (207).
5. The system (200) as claimed in claim 1, wherein the multitude of load parameters include percentage load of the motor (25%, 50%, 75%, 100%, 125%) and heat losses pertaining to the load.
6. A method for health monitoring of an electric motor (100) implemented using a system (200), the method comprising:
- receiving input dataset and fetching them to an input device (205);
- collecting and processing data by a data processing device (207);
- exporting processed data to an artificial intelligence device (208) by the data processing device (207);
- training and testing of the dataset by the artificial intelligence device (208);
- fine tuning of the results generated as a result of training and testing by the artificial intelligence device (208);
- validating the results generated by the artificial intelligence device (208) by a validation device (209); and
- generating live temperature of a rotor and a stator while the electric motor is in operation.
7. The method as claimed in claim 6, wherein the input data set received by the input device (205) is generated by a data generation device (206) following a principle of fluid dynamics.
8. The method as claimed in claim 6, wherein temperature predicted across a cross section of one plane of the electric motor (100) is mapped throughout all cross sections of the electric motor (100) using multiple neural networks in between by the artificial intelligence device (208).