A motor fault diagnosis method, system, device and storage medium

Abstract

The application discloses a motor fault diagnosis method, system, device and storage medium, which is applied to the motor technical field and comprises the following steps: detecting three-phase voltage and leakage current of a motor; performing feature extraction based on the three-phase voltage and the leakage current of the motor to obtain a feature extraction result; and analyzing the feature extraction result to obtain a fault diagnosis result of the motor; wherein the types of the fault diagnosis result include the following: no fault of the motor, abnormal main insulation of the motor, abnormal turn-to-turn insulation of the motor and abnormal bearing of the motor. The scheme of the application can conveniently and effectively perform motor fault diagnosis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric machines, in particular to an electric machine fault diagnosis method, system, device and storage medium. BACKGROUND

[0002] Variable frequency electric machines are key equipment in many fields such as industrial production, high-speed railway, ship driving, new energy vehicles, etc. The unexpected shutdown problem caused by electric machine faults can cause a lot of economic losses, and even serious safety consequences, so it is particularly important to improve the reliability of electric machine operation. With the technological progress of power semiconductor devices, high-frequency switching operation has become possible, which has also greatly improved the performance of PWM (Pulse Width Modulation) inverter controlled electric machines. However, the fast switching of power electronic devices can cause high dv / dt values, which can significantly increase the electrical stress on the insulation material. Voltage harmonics and high dv / dt can increase the dielectric loss in the stator winding and core insulation, shortening the service life of the insulation material. Also, it can cause changes in bearing oil film capacitance and resistance (bearing electrochemical corrosion, wear), thereby affecting the service life of the bearing. According to statistics, the most common forms of faults in electric machines are stator insulation faults, bearing faults and rotor faults. For stator insulation faults, it can cause turn-to-turn short circuits and even insulation failures and other serious faults; and bearing faults can cause the failure of the entire electrical and mechanical system.

[0003] Currently, electric machine stator insulation faults and bearing faults are mainly detected by periodic disassembly and maintenance in an offline manner, which is not real-time and requires a lot of human resources.

[0004] In summary, how to conveniently and effectively diagnose electric machine faults is a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0005] The purpose of the present application is to provide an electric machine fault diagnosis method, system, device and storage medium to conveniently and effectively diagnose electric machine faults.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an electric machine fault diagnosis method, comprising:

[0008] detecting three-phase voltages and leakage currents of the electric machine;

[0009] performing feature extraction based on the three-phase voltages and leakage currents of the electric machine to obtain a feature extraction result;

[0010] analyzing the feature extraction result to obtain a fault diagnosis result of the motor; wherein the fault diagnosis result includes: no fault of the motor, abnormality of main insulation of the motor, abnormality of turn-to-turn insulation of the motor, and abnormality of bearing of the motor.

[0011] In an implementation, analyzing the feature extraction result to obtain a fault diagnosis result of the motor includes:

[0012] inputting the feature extraction result into a trained neural network model, and obtaining the fault diagnosis result of the motor through the neural network model.

[0013] In an implementation, detecting three-phase voltage and leakage current of the motor includes:

[0014] detecting three-phase voltage and leakage current of the motor for a first time duration whenever sampling is triggered;

[0015] determining whether the working condition of the motor is stable within the first time duration;

[0016] if yes, performing the operation of performing feature extraction based on the three-phase voltage and leakage current of the motor to obtain a feature extraction result;

[0017] if no, discarding the detected three-phase voltage and leakage current of the motor for the first time duration.

[0018] In an implementation, determining whether the working condition of the motor is stable within the first time duration includes:

[0019] determining whether the phase voltage effective value and voltage fundamental frequency of the motor remain stable within the first time duration;

[0020] if yes, determining that the working condition of the motor is stable, and otherwise, determining that the working condition of the motor is unstable.

[0021] In an implementation, after determining that the working condition of the motor is stable, inputting the feature extraction result into a trained neural network model, and obtaining the fault diagnosis result of the motor through the neural network model includes:

[0022] inputting the feature extraction result into a trained neural network model corresponding to the current working condition, and obtaining the fault diagnosis result of the motor through the neural network model corresponding to the current working condition.

[0023] In an implementation, performing feature extraction based on the three-phase voltage and leakage current of the motor to obtain a feature extraction result includes:

[0024] performing frequency spectrum analysis on the leakage current of the motor to obtain a leakage current frequency spectrum of the motor;

[0025] averaging the three-phase voltage of the motor as a common-mode voltage, and performing a spectrum analysis on the common-mode voltage to obtain a common-mode voltage spectrum of the motor;

[0026] determining a resonance frequency in a first frequency range and a resonance point amplitude at the resonance frequency based on the leakage current spectrum of the motor, and determining a resonance frequency in a second frequency range and a resonance point amplitude at the resonance frequency;

[0027] determining a common-mode voltage amplitude corresponding to a frequency of a common-mode voltage fundamental frequency, a current amplitude, and a phase difference between the voltage and the current at the common-mode voltage fundamental frequency based on the leakage current spectrum and the common-mode voltage spectrum of the motor;

[0028] determining a main insulation equivalent capacitance value and a main insulation equivalent resistance value based on the determined common-mode voltage amplitude corresponding to the frequency of the common-mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common-mode voltage fundamental frequency;

[0029] adding the resonance frequency in the first frequency range and the resonance point amplitude at the resonance frequency, the resonance frequency in the second frequency range and the resonance point amplitude at the resonance frequency, the main insulation equivalent capacitance value, and the main insulation equivalent resistance value to the obtained feature extraction result.

[0030] In an embodiment, the method further comprises:

[0031] determining a time-domain oscillation cycle mean and a time-domain oscillation cycle standard deviation of the leakage current, and a time-domain peak-to-peak value mean and a time-domain peak-to-peak value standard deviation of the leakage current based on the leakage current of the motor, and adding them to the feature extraction result.

[0032] In an embodiment, determining a main insulation equivalent capacitance value and a main insulation equivalent resistance value based on the determined common-mode voltage amplitude corresponding to the frequency of the common-mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common-mode voltage fundamental frequency comprises:

[0033] determining the main insulation equivalent capacitance value based on

[0034] determining the main insulation equivalent resistance value based on

[0035] wherein C eq is the main insulation equivalent capacitance value, R eq is the main insulation equivalent resistance value, Z is the main insulation equivalent impedance, and ​​, Uc, Ic and θc are the common-mode voltage amplitude, current amplitude and phase difference between voltage and current respectively corresponding to the fundamental frequency of common-mode voltage c is the fundamental frequency of common-mode voltage.

[0036] In a second aspect, the present application provides a motor fault diagnosis system, comprising:

[0037] a sampling module configured to detect three-phase voltage and leakage current of the motor;

[0038] a feature extraction module configured to perform feature extraction based on the three-phase voltage and leakage current of the motor to obtain a feature extraction result;

[0039] a fault diagnosis module configured to analyze the feature extraction result to obtain a fault diagnosis result of the motor; wherein the types of the fault diagnosis result include: no fault of the motor, abnormality of main insulation of the motor, abnormality of turn-to-turn insulation of the motor, and abnormality of bearing of the motor.

[0040] In an embodiment, the fault diagnosis module is specifically configured to:

[0041] input the feature extraction result into a trained neural network model to obtain the fault diagnosis result of the motor through the neural network model.

[0042] In an embodiment, the sampling module comprises:

[0043] a sampling unit configured to detect three-phase voltage and leakage current of the motor for a first time duration whenever sampling is triggered;

[0044] a working condition judgment unit configured to judge whether the working condition of the motor is stable within the first time duration;

[0045] if yes, trigger the feature extraction module;

[0046] if no, trigger a discard unit configured to discard the detected three-phase voltage and leakage current of the motor for the first time duration.

[0047] In an embodiment, the feature extraction module comprises:

[0048] a leakage current spectrum analysis unit configured to perform spectrum analysis on the leakage current of the motor to obtain a leakage current spectrum of the motor;

[0049] a common-mode voltage spectrum analysis unit configured to take the average value of the three-phase voltage of the motor as common-mode voltage and perform spectrum analysis on the common-mode voltage to obtain a common-mode voltage spectrum of the motor;

[0050] a resonance point analysis unit configured to determine a resonance frequency in the first frequency range and a resonance point amplitude at the resonance frequency based on the leakage current spectrum of the motor, and determine a resonance frequency in the second frequency range and a resonance point amplitude at the resonance frequency based on the leakage current spectrum of the motor;

[0051] a main insulation impedance analysis unit configured to determine a common mode voltage amplitude corresponding to a frequency of a common mode voltage fundamental frequency, a current amplitude, and a phase difference between the voltage and the current at the common mode voltage fundamental frequency based on the leakage current spectrum of the motor and the common mode voltage spectrum, and determine a main insulation equivalent capacitance value and a main insulation equivalent resistance value based on the determined common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common mode voltage fundamental frequency;

[0052] a feature extraction result determination unit configured to determine the resonance frequency in the first frequency range and the resonance point amplitude at the resonance frequency, the resonance frequency in the second frequency range and the resonance point amplitude at the resonance frequency, the main insulation equivalent capacitance value, and the main insulation equivalent resistance value as the obtained feature extraction result.

[0053] In an embodiment, the motor fault diagnosis device further comprises a time domain analysis unit configured to:

[0054] determine a time domain oscillation cycle mean value and a time domain oscillation cycle standard deviation of the leakage current, and a time domain peak to peak value mean value and a time domain peak to peak value standard deviation of the leakage current based on the leakage current of the motor, and add the time domain oscillation cycle mean value and the time domain oscillation cycle standard deviation, and the time domain peak to peak value mean value and the time domain peak to peak value standard deviation to the feature extraction result.

[0055] In a third aspect, the present application provides a motor fault diagnosis device, comprising:

[0056] a memory configured to store a computer program;

[0057] a processor configured to execute the computer program to implement the steps of the motor fault diagnosis method as described above.

[0058] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the motor fault diagnosis method as described above.

[0059] With the technical solution provided by the embodiment of the application, considering that the distributed parameters and high-frequency characteristics of the motor system result in a complex impedance network in the system, and the high-frequency multi-modal switching oscillation generated by the high-speed switching excitation of the power electronics on the distributed parameters of the motor system, it provides feasibility for diagnosing the motor insulation and bearing health state by using the high-frequency switching oscillation signal generated by the motor current self-excitation. Specifically, by detecting the three-phase voltage and leakage current of the motor, especially the leakage current, the insulation and bearing degradation problems of the motor can be effectively reflected. And in order to facilitate analysis, feature extraction is performed based on the three-phase voltage and leakage current of the motor to obtain a feature extraction result, and then the feature extraction result is analyzed, so that the fault diagnosis result of the motor can be effectively obtained. In order to realize more accurate classification, the types of the fault diagnosis result need to include: motor fault-free, motor main insulation abnormal, motor turn-to-turn insulation abnormal, and motor bearing abnormal, that is, through the scheme of the application, the classification of motor main insulation / turn-to-turn insulation and bearing abnormality can be effectively realized. In summary, the scheme of the application can conveniently and effectively diagnose the motor fault. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0061] Figure 1 The implementation flowchart of the motor fault diagnosis method provided by a specific embodiment of the application is shown in the figure.

[0062] Figure 2 The equivalent circuit schematic diagram of the motor in a specific embodiment of the application is shown in the figure.

[0063] Figure 3 The motor fault diagnosis principle schematic diagram in a specific embodiment of the application is shown in the figure.

[0064] Figure 4 The time-domain waveform diagram of the leakage current of the motor detected in a specific embodiment of the application is shown in the figure.

[0065] Figure 5 The single leakage current waveform in the time-domain waveform diagram of the leakage current of the motor detected in a specific embodiment of the application is shown in the figure.

[0066] Figure 6 The leakage current frequency spectrum diagram of the motor obtained in a specific embodiment of the application is shown in the figure.

[0067] Figure 7A schematic diagram of a motor leakage current spectrum and a phase voltage spectrum in one embodiment of the present application;

[0068] Figure 8 A structural schematic diagram of a motor fault diagnosis system provided in one embodiment of the present application;

[0069] Figure 9 A structural schematic diagram of a motor fault diagnosis device provided in one embodiment of the present application;

[0070] Figure 10 A structural schematic diagram of a computer readable storage medium. EMBODIMENT

[0071] The core of the present application is to provide a motor fault diagnosis method, system, device and storage medium, which can facilitate and effectively perform motor fault diagnosis.

[0072] In order to enable persons skilled in the art to better understand the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor fall within the scope of protection of the present application.

[0073] Reference should be made to Figure 1 , Figure 1 An implementation flowchart of a motor fault diagnosis method provided in one embodiment of the present application, which can include the following steps:

[0074] Step S101: detecting three-phase voltage and leakage current of the motor.

[0075] The present application considers that the distributed parameters and high-frequency characteristics of the motor system result in a complex impedance network in the system, which not only includes motor stator resistance and inductance, but also includes distributed impedance between the device and the ground, which can be referred to Figure 2 , which is an equivalent circuit diagram of the motor. Figure 2 Cp in the formula is the inter-turn equivalent capacitance of the coil of the motor, C eq is the equivalent capacitance of the motor to the ground, C B1 and C B2 are bearing oil film capacitances, R B1 and R B2 are bearing oil film resistances, L C is a cable inductance, C L is a cable insulation capacitance, C SR is a parasitic capacitance between the stator winding and the motor rotor, C RFI dv / dt for the bearing current. When the IGBT switch in the frequency converter switches from off to on, the bus voltage is directly applied to the motor coil, which can cause high-frequency oscillation of the current in the circuit. If the motor main insulation (i.e. the insulation between the winding and the ground), the turn-to-turn insulation (the insulation between the windings), and the bearing oil film capacitor are deteriorated, the element parameters in the complex impedance network will change, thereby causing the oscillation of the leakage current. Therefore, the leakage current waveform can effectively realize the fault diagnosis of the motor. And the application considers that the three-phase voltage of the motor can also assist in the fault diagnosis of the motor, so in the application, the three-phase voltage and the leakage current of the motor are detected.

[0076] For reference Figure 3 , it is a schematic diagram of the motor fault diagnosis principle in a specific embodiment. The three-phase voltage of the motor is detected by three phase voltage sensors, and the leakage current of the motor is collected by the leakage current sensor sleeved on the motor input terminal. Subsequent analysis and processing are performed by the motor fault diagnosis device. In addition, Figure 3 , the communication interface of the fault diagnosis device is also shown, which represents that the fault diagnosis result can be output based on the communication interface.

[0077] When detecting the three-phase voltage and the leakage current of the motor, it can be detected periodically. For example, in one occasion, the detection frequency can reach more than 30MHz. In addition, the current sensor sleeved at the motor input terminal can be used to collect the leakage current of the motor U, V and W three-phase. Since the motor insulation weakening feature usually focuses on the leakage current characteristics of tens to hundreds of KHz, the test data shows that the sensitive detection mode of the oil film capacitor change is in the megahertz level. Therefore, in the application, the leakage current sensor needs to reach the sampling requirement of milliamperes, and the sensor bandwidth should meet the requirement of no obvious signal attenuation below 30MHz, so as to effectively guarantee the stability and accuracy of the original data, which is beneficial to guarantee the accuracy of the fault diagnosis result. In addition, the aperture of the leakage current sensor needs to reach millimeters (where x is the large wire diameter of the motor) so that the leakage current sensor can be sleeved on the three large wires of the motor, and the signal frequency band is 1kHz-30MHz.

[0078] The phase voltage sensor can be arranged at the motor input terminal to collect the ground phase voltage of the motor U phase, V phase and W phase, respectively. The range of the phase voltage sensor needs to meet the phase voltage measurement requirement of the motor model, and the signal frequency band usually needs to reach 10Hz-500kHz.

[0079] In one specific embodiment of the application, step S101 can include:

[0080] detecting three-phase voltages and leakage currents of the motor for a first time duration whenever sampling is triggered;

[0081] determining whether the working condition of the motor is stable within the first time duration;

[0082] if yes, performing feature extraction based on the three-phase voltages and the leakage currents of the motor to obtain a feature extraction result;

[0083] if no, discarding the detected three-phase voltages and the leakage currents of the motor for the first time duration.

[0084] The embodiment considers that, in order to facilitate subsequent feature extraction, three-phase voltages and leakage currents can be collected for a period of time, and subsequent feature extraction can be implemented based on the three-phase voltages and the leakage currents for the period of time. In this embodiment, three-phase voltages and leakage currents of the motor are detected for a first time duration whenever sampling is triggered. For example, in one occasion, the frequency of sampling reaches 30 MHz or above, and the collection length of each data is set to 0.1 s, that is, the first time duration is set to 0.1 s.

[0085] In addition, in this embodiment, the specific timing of triggering sampling can be set and adjusted according to actual needs. For example, after detecting three-phase voltages and leakage currents of the motor for a first time duration whenever sampling is triggered, the next sampling can be triggered after a period of time. For another example, after detecting three-phase voltages and leakage currents of the motor for a first time duration whenever sampling is triggered, the next sampling can be triggered immediately.

[0086] After detecting three-phase voltages and leakage currents of the motor for a first time duration, it is determined in this embodiment whether the working condition of the motor is stable within the first time duration. This is because the working condition of the motor also has a certain degree of influence on the leakage current feature. In order to shield the influence of the motor working condition and guarantee the reliability of the diagnosis result, the detected three-phase voltages and leakage currents of the motor for the first time duration should be detected under stable working condition. In this embodiment, it is determined whether the working condition of the motor is stable within the first time duration. If the working condition is stable, the subsequent operation of step S102 can be normally performed. Correspondingly, if the working condition of the motor is not stable within the first time duration, the detected three-phase voltages and leakage currents of the motor for the first time duration can be discarded to avoid producing false fault diagnosis results.

[0087] The specific way of determining whether the working condition of the motor is stable can be various. For example, in one specific embodiment of the application, determining whether the working condition of the motor is stable within the first time duration can specifically include:

[0088] determining whether the phase voltage effective value and the voltage fundamental frequency of the motor remain stable within the first time duration;

[0089] If yes, it is determined that the working condition of the motor is stable, otherwise it is determined that the working condition of the motor is unstable.

[0090] As described above, the working condition of the motor will have a certain degree of influence on the leakage current characteristics. Specifically, the main influencing factors are the speed and torque of the motor, so in order to ensure the reliability of the diagnosis result, the influence of the speed and torque of the motor needs to be shielded. The torque of the motor will be reflected in the phase voltage effective value, and the voltage fundamental frequency is related to the speed of the motor, so in this embodiment, it is determined whether the phase voltage effective value and the voltage fundamental frequency of the motor remain stable within the first time period. If yes, it means that the speed and torque of the motor are relatively stable within the first time period, so it can be considered that the working condition of the motor is stable, otherwise it can be determined that the working condition of the motor is unstable.

[0091] The specific way of determining whether the phase voltage effective value and the voltage fundamental frequency of the motor remain stable within the first time period can be various. Taking the phase voltage effective value as an example, for example, the average a of the phase voltage effective value of the motor within the first time period can be determined, and if the phase voltage effective value of the motor remains within the range of a±c in more than b% of the time period within the first time period, it can be considered that the phase voltage effective value of the motor remains stable within the first time period, otherwise it is considered to be unstable. For another example, the variation range of the phase voltage effective value of the motor within the first time period can be determined, and if the variation range exceeds the set threshold, it can be considered that the phase voltage effective value of the motor remains stable within the first time period, otherwise it is considered to be unstable. According to the same principle, it can also be determined whether the voltage fundamental frequency of the motor remains stable within the first time period.

[0092] In the above embodiment, whether the working condition of the motor is stable is determined based on the phase voltage effective value and the voltage fundamental frequency of the motor, and in some embodiments, the way of calculating the speed and torque of the motor is various and not complex, so the speed and torque of the motor within the first time period can also be determined, and then whether the speed and torque of the motor remain stable within the first time period is determined to determine whether the working condition of the motor is stable.

[0093] Step S102: performing feature extraction based on the three-phase voltage and the leakage current of the motor to obtain a feature extraction result.

[0094] The present application considers that if the analysis is directly based on the three-phase voltage and the leakage current of the motor, the convenience of the analysis cannot be guaranteed, and it is not conducive to guarantee the accuracy of the fault diagnosis result of the motor, so the feature extraction can be performed based on the three-phase voltage and the leakage current of the motor to obtain a feature extraction result, and then the feature extraction result is analyzed to obtain the fault diagnosis result of the motor.

[0095] Of course, the specific way of feature extraction can be various, as long as the features that are conducive to obtaining accurate motor fault diagnosis results can be extracted.

[0096] For example, in one specific embodiment of the present application, step S102 can include the following steps:

[0097] Step one: perform frequency spectrum analysis on the motor's leakage current to obtain the motor's leakage current frequency spectrum; take the average value of the motor's three-phase voltage as the common-mode voltage, and perform frequency spectrum analysis on the common-mode voltage to obtain the motor's common-mode voltage frequency spectrum;

[0098] Step two: based on the motor's leakage current frequency spectrum, determine the resonance frequency within the first frequency range and the resonance point amplitude at the resonance frequency, and determine the resonance frequency within the second frequency range and the resonance point amplitude at the resonance frequency;

[0099] Step three: based on the motor's leakage current frequency spectrum and the common-mode voltage frequency spectrum, determine the common-mode voltage amplitude corresponding to the common-mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common-mode voltage fundamental frequency;

[0100] Step four: based on the determined common-mode voltage amplitude corresponding to the common-mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common-mode voltage fundamental frequency, determine the main insulation equivalent capacitance value and the main insulation equivalent resistance value;

[0101] Step five: take the resonance frequency within the first frequency range and the resonance point amplitude at the resonance frequency, the resonance frequency within the second frequency range and the resonance point amplitude at the resonance frequency, the main insulation equivalent capacitance value, and the main insulation equivalent resistance value as the obtained feature extraction result.

[0102] This embodiment takes into account that the features in the frequency domain of the motor's three-phase voltage and leakage current are conducive to analyzing more accurate motor fault diagnosis results, so the motor is subjected to frequency spectrum analysis to obtain the motor's common-mode voltage frequency spectrum and leakage current frequency spectrum. In particular, the motor's leakage current frequency spectrum is conducive to effectively distinguishing insulation abnormalities and bearing abnormalities.

[0103] For a specific embodiment of the motor's leakage current time-domain waveform detected in Figure 4 is a single leakage current waveform in the time-domain waveform. Figure 5 and Figure 4 and Figure 5The horizontal axis of each of the time-domain waveforms represents time, and the vertical axis represents the amplitude of the leakage current in the time domain. For example, in this embodiment, when sampling is triggered, the three-phase voltage and the leakage current of the motor are detected for a first time duration. For example, the sampling frequency is greater than 30 MHz, and the first time duration is set to 0.1 second. For the 0.1-second sampling window, a plurality of oscillating current waveforms are included, Figure 4 For example, the time-domain waveform of the leakage current of the motor for the 0.1 second is shown in FIG. 6. Figure 5 For example, the time-domain waveform of the leakage current of the motor for the 0.1 second is shown in FIG. 6.

[0104] The time-domain waveform of the leakage current of the motor is subjected to fast Fourier transform, and the leakage current spectrum of the motor is obtained. For example, the leakage current spectrum of the motor obtained in this embodiment is shown in FIG. 7. Figure 4 The horizontal axis of each of the time-domain waveforms represents time, and the vertical axis represents the amplitude of the leakage current in the time domain. For example, in this embodiment, when sampling is triggered, the three-phase voltage and the leakage current of the motor are detected for a first time duration. For example, the sampling frequency is greater than 30 MHz, and the first time duration is set to 0.1 second. For the 0.1-second sampling window, a plurality of oscillating current waveforms are included, Figure 6 For example, the time-domain waveform of the leakage current of the motor for the 0.1 second is shown in FIG. 6. Figure 6 The horizontal axis of each of the time-domain waveforms represents time, and the vertical axis represents the amplitude of the leakage current in the time domain. For example, in this embodiment, when sampling is triggered, the three-phase voltage and the leakage current of the motor are detected for a first time duration. For example, the sampling frequency is greater than 30 MHz, and the first time duration is set to 0.1 second. For the 0.1-second sampling window, a plurality of oscillating current waveforms are included,

[0105] When the parameters of the elements in the complex impedance network change, the resonance point frequency in Figure 6 changes, and the resonance point amplitude also changes. The resonance point modal change caused by the motor insulation is different from the resonance point modal change caused by the oil film capacitance. Specifically, the resonance point frequency corresponding to the motor insulation deterioration is distributed in a low frequency band, and the resonance point frequency corresponding to the oil film capacitance is distributed in a high frequency band, usually greater than MHz, so that the two characteristics do not interfere with each other.

[0106] In this embodiment, the resonance frequency in the first frequency range and the resonance point amplitude at the resonance frequency can be determined based on the leakage current spectrum of the motor. The first frequency range is a low frequency range, for example, in the example of Figure 6 , the determined resonance frequency in the first frequency range is f1, and the resonance point amplitude at the resonance frequency is A1. The second frequency range is a high frequency range, for example, in the example of Figure 6 , the determined resonance frequency in the second frequency range is f2, and the resonance point amplitude at the resonance frequency is A2.

[0107] As can be understood from the above analysis, f1 and A1 in the normal case of the motor are different from f1 and A1 in the case of motor insulation deterioration, and f2 and A2 in the normal case of the motor are different from f2 and A2 in the case of bearing abnormality.

[0108] The implementation further considers that motor insulation deterioration includes motor ground main insulation deterioration and turn-to-turn insulation deterioration, both of which have different effects on the overall impedance of the motor, and the main insulation deterioration has a greater effect on the motor ground impedance, so the ground impedance can be further identified to distinguish the main insulation deterioration and the turn-to-turn insulation deterioration, and the accuracy of fault diagnosis is improved.

[0109] The ground impedance can be reflected by the main insulation equivalent capacitance value and the main insulation equivalent resistance value. Therefore, in this implementation, the corresponding common-mode voltage amplitude when the frequency is the common-mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common-mode voltage fundamental frequency are determined based on the motor leakage current spectrum and the common-mode voltage spectrum.

[0110] The common-mode voltage spectrum needs to be determined based on the three-phase voltage. Specifically, the three-phase voltage of the motor is obtained through phase voltage detection, and then the average value is taken as the common-mode voltage. It can be expressed as:

[0111] Here, U a , U b , and U c represent the three-phase voltage, and U is the average value of the three-phase voltage. After obtaining the common-mode voltage, the common-mode voltage needs to be analyzed to obtain the frequency spectrum, which is called the common-mode voltage spectrum of the motor.

[0112] For reference Figure 7 , Figure 7 The right side is the motor leakage current spectrum, and the left side is the common-mode voltage spectrum. For the motor leakage current spectrum, the corresponding current amplitude when the frequency is the common-mode voltage fundamental frequency fc needs to be determined, Figure 7 which is denoted as Ic in the formula. Correspondingly, for the common-mode voltage spectrum of the motor, the corresponding common-mode voltage amplitude when the frequency is the common-mode voltage fundamental frequency fc needs to be determined, Figure 7 which is denoted as Uc in the formula. In addition, the phase difference between the voltage and the current at the frequency fc needs to be determined.

[0113] After obtaining the corresponding common-mode voltage amplitude, current amplitude, and phase difference between voltage and current when the frequency is the common-mode voltage fundamental frequency, the main insulation equivalent capacitance value and the main insulation equivalent resistance value can be determined.

[0114] Finally, in this implementation, the resonance frequency f1 in the first frequency range and the resonance point amplitude A1 at the resonance frequency, the resonance frequency f2 in the second frequency range and the resonance point amplitude A2 at the resonance frequency, the main insulation equivalent capacitance value, and the main insulation equivalent resistance value are taken as the obtained feature extraction results.

[0115] Based on the common-mode voltage amplitude corresponding to the common-mode voltage fundamental frequency, the current amplitude and the phase difference between the voltage and the current, the main insulation equivalent capacitance value and the main insulation equivalent resistance value are determined, and the specific calculation method can be various, for example, in one specific embodiment of the present application, step four can specifically include:

[0116] Based on The main insulation equivalent capacitance value is determined;

[0117] Based on The main insulation equivalent resistance value is determined;

[0118] Wherein, C eq is the main insulation equivalent capacitance value, R eq is the main insulation equivalent resistance value, Z is the main insulation equivalent impedance, and , Uc, Ic and θc are the common-mode voltage amplitude corresponding to the common-mode voltage fundamental frequency, the current amplitude and the phase difference between the voltage and the current respectively, f c is the common-mode voltage fundamental frequency.

[0119] In this embodiment, the main insulation equivalent capacitance value C eq and the main insulation equivalent resistance value R eq can be conveniently calculated.

[0120] Further, in one specific embodiment of the present application, it can further include:

[0121] Based on the leakage current of the motor, the time domain oscillation cycle mean and the time domain oscillation cycle standard deviation of the leakage current, and the time domain peak-peak value mean and the time domain peak-peak value standard deviation of the leakage current are determined, and are all added to the feature extraction result.

[0122] This embodiment considers that although the features in the frequency domain are helpful to analyze the more accurate motor fault diagnosis result, some features in the time domain are also helpful to improve the accuracy of the fault diagnosis result to a certain extent, and through experimental analysis, the applicant finds that the time domain oscillation cycle and the peak-peak value of the leakage current are helpful to improve the accuracy of the motor fault diagnosis result.

[0123] And refer to Figure 4 and Figure 5 , for Figure 4 This 0.1 second acquisition window will contain a plurality of oscillating current waveforms, Figure 5 It is shown that a single leakage current waveform, and Figure 5The time domain oscillation period Ta and the peak-peak value Arange of the single leakage current waveform are shown in the figure. Therefore, in this embodiment, the time domain oscillation period mean value and the standard deviation thereof of the leakage current are calculated for the time domain oscillation period, and the peak-peak value mean value and the standard deviation thereof are calculated for the peak-peak value, and both are added to the feature extraction result.

[0124] Step S103: analyzing the feature extraction result to obtain the fault diagnosis result of the motor; wherein the types of the fault diagnosis result include: no fault of the motor, abnormality of the main insulation of the motor, abnormality of the turn-to-turn insulation of the motor, and abnormality of the bearing of the motor.

[0125] The feature extraction result is analyzed to obtain the fault diagnosis result of the motor. The specific analysis method used can be various, for example, f1 and A1 in the feature extraction result can be compared with f1 and A1 in the pre-determined normal condition of the motor to determine whether there is insulation abnormality, and f2 and A2 in the feature extraction result can be compared with f2 and A2 in the pre-determined normal condition of the motor to determine whether there is bearing abnormality of the motor.

[0126] In a specific embodiment of the present application, step S103 can specifically include:

[0127] The feature extraction result is input into the trained neural network model to obtain the fault diagnosis result of the motor through the neural network model.

[0128] This embodiment considers that, since there are usually a large number of features in the feature extraction result, the neural network can be used to conveniently realize comprehensive evaluation and obtain a more accurate fault diagnosis result. The neural network model is a complex network system formed by a large number of simple processing units (neurons) widely connected to each other, has large-scale parallelism, distributed storage and processing, self-organization, self-adaptation and self-learning ability, and is particularly suitable for processing information processing problems that need to consider multiple factors and conditions at the same time and are inaccurate and fuzzy. Compared with methods such as artificial scoring (for example, analytic hierarchy process) for comprehensive evaluation of multiple features, the neural network model trained based on a large amount of test data can effectively reduce the interference caused by subjective factors and ensure the accuracy of the fault diagnosis result.

[0129] In a specific embodiment of the present application, after determining that the working condition of the motor is stable, the feature extraction result is input into the trained neural network model to obtain the fault diagnosis result of the motor through the neural network model, which can specifically include:

[0130] The feature extraction result is input into the trained neural network model corresponding to the current working condition to obtain the fault diagnosis result of the motor through the neural network model corresponding to the current working condition.

[0131] The embodiment considers that, in order to further guarantee the accuracy of the fault diagnosis result and exclude the interference of the working condition, for different working conditions of the motor, a corresponding neural network model can be trained in advance, so that when sampling is triggered subsequently, the three-phase voltage and the leakage current of the motor are detected for a first time length, and the feature extraction result of this time is obtained, the feature extraction result needs to be input into the neural network model corresponding to the working condition this time. As described above, in the scheme of the application, the motor working condition can be measured by using the speed and torque, or by using the voltage effective value and the fundamental frequency. Taking the speed and torque as an example, in actual application, in order to avoid setting too many neural network models, the speed can be divided into several interval ranges, and the torque can also be divided into several interval ranges, and then by combination, each working condition can be obtained, and then a corresponding number of neural network models can be trained according to the different working conditions.

[0132] The technical scheme provided by the embodiment of the application considers that the distributed parameters and high-frequency characteristics of the motor system result in a complex impedance network in the system, and the high-frequency multi-modal switching oscillation generated by the high-frequency switching oscillation signal of the motor current self-excitation provides feasibility for diagnosing the motor insulation and bearing health status. Specifically, by detecting the three-phase voltage and the leakage current of the motor, especially the leakage current, the insulation and bearing degradation problems of the motor can be effectively reflected. And in order to facilitate analysis, feature extraction can be performed based on the three-phase voltage and the leakage current of the motor to obtain a feature extraction result, and then the feature extraction result is analyzed, and the fault diagnosis result of the motor can be effectively obtained. In order to realize more accurate classification, the types of the fault diagnosis result need to include: motor fault-free, motor main insulation abnormal, motor turn-to-turn insulation abnormal, and motor bearing abnormal, that is, through the scheme of the application, the classification of the motor main insulation / turn-to-turn insulation and bearing abnormality can be effectively realized. In summary, the scheme of the application can conveniently and effectively perform motor fault diagnosis.

[0133] Corresponding to the above method embodiment, the embodiment of the application also provides a motor fault diagnosis system, which can be mutually corresponding and referred to above.

[0134] Referring to Figure 8 As shown in FIG. 8, it is a structural schematic diagram of a motor fault diagnosis system in the application, which comprises:

[0135] The sampling module 801 is configured to detect the three-phase voltage and the leakage current of the motor.

[0136] The feature extraction module 802 is configured to perform feature extraction based on the three-phase voltage and the leakage current of the motor to obtain a feature extraction result.

[0137] The fault diagnosis module 803 is configured to analyze the feature extraction result to obtain a fault diagnosis result of the motor, wherein the fault diagnosis result includes: no fault of the motor, abnormality of main insulation of the motor, abnormality of turn-to-turn insulation of the motor, and abnormality of a bearing of the motor.

[0138] In an embodiment of the present application, the fault diagnosis module is specifically configured to:

[0139] input the feature extraction result into a trained neural network model, and obtain the fault diagnosis result of the motor through the neural network model.

[0140] In an embodiment of the present application, the sampling module 801 includes:

[0141] The sampling unit is configured to detect the three-phase voltage and the leakage current of the motor in the first time length each time the sampling is triggered.

[0142] The working condition judgment unit is configured to judge whether the working condition of the motor is stable in the first time length.

[0143] If yes, the feature extraction module 802 is triggered.

[0144] If no, the discarding unit is triggered to discard the three-phase voltage and the leakage current of the motor in the first time length detected this time.

[0145] In an embodiment of the present application, the working condition judgment unit is specifically configured to:

[0146] judge whether the phase voltage effective value and the voltage fundamental frequency of the motor remain stable in the first time length.

[0147] If yes, it is determined that the working condition of the motor is stable, otherwise it is determined that the working condition of the motor is unstable.

[0148] In an embodiment of the present application, the fault diagnosis module 803 is specifically configured to:

[0149] input the feature extraction result into a trained neural network model corresponding to the current working condition, and obtain the fault diagnosis result of the motor through the neural network model corresponding to the current working condition.

[0150] In an embodiment of the present application, the feature extraction module 802 includes:

[0151] The leakage current spectrum analysis unit is configured to perform spectrum analysis on the leakage current of the motor to obtain a leakage current spectrum of the motor.

[0152] The common-mode voltage spectrum analysis unit is configured to take the average value of the three-phase voltage of the motor as a common-mode voltage, and perform spectrum analysis on the common-mode voltage to obtain a common-mode voltage spectrum of the motor.

[0153] a resonance point analysis unit configured to determine a resonance frequency in the first frequency range and a resonance point amplitude at the resonance frequency based on the motor leakage current spectrum, and determine a resonance frequency in the second frequency range and a resonance point amplitude at the resonance frequency based on the motor leakage current spectrum;

[0154] a main insulation impedance analysis unit configured to determine a common mode voltage amplitude corresponding to a frequency of a common mode voltage fundamental frequency, a current amplitude, and a phase difference between the voltage and the current at the common mode voltage fundamental frequency based on the motor leakage current spectrum and the common mode voltage spectrum, and determine a main insulation equivalent capacitance value and a main insulation equivalent resistance value based on the determined common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common mode voltage fundamental frequency;

[0155] a feature extraction result determination unit configured to determine the resonance frequency in the first frequency range and the resonance point amplitude at the resonance frequency, the resonance frequency in the second frequency range and the resonance point amplitude at the resonance frequency, the main insulation equivalent capacitance value, and the main insulation equivalent resistance value as the obtained feature extraction result.

[0156] In one embodiment of the present application, further comprising a time domain analysis unit configured to:

[0157] determine a time domain oscillation cycle mean value and a time domain oscillation cycle standard deviation of the motor leakage current, and a time domain peak-to-peak value mean value and a time domain peak-to-peak value standard deviation of the motor leakage current based on the motor leakage current, and add them to the feature extraction result.

[0158] In one embodiment of the present application, the main insulation impedance analysis unit determines the main insulation equivalent capacitance value and the main insulation equivalent resistance value based on the determined common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current at the common mode voltage fundamental frequency, comprising:

[0159] determining the main insulation equivalent capacitance value based on determining the main insulation equivalent resistance value based on

[0160]

[0161] wherein C eq is the main insulation equivalent capacitance value, R eq is the main insulation equivalent resistance value, Z is the main insulation equivalent impedance, and , Uc, Ic, and θc are the common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude, and the phase difference between the voltage and the current, respectively, and f c is the common mode voltage fundamental frequency. ​​

[0162] Corresponding to the above method and system embodiments, the embodiments of the present application also provide a motor fault diagnosis device and a computer readable storage medium, which can be mutually corresponding with the above.

[0163] Referring to Figure 9 As shown in the figure, the device can include:

[0164] a sensor 901 for signal sampling;

[0165] a memory 902 for storing a computer program;

[0166] a processor 903 for executing the computer program to implement the steps of the motor fault diagnosis method in any of the above embodiments.

[0167] Referring to Figure 10 The computer readable storage medium 90 has a computer program 91 stored thereon, and the computer program 91 is executed by the processor to implement the steps of the motor fault diagnosis method in any of the above embodiments. The computer readable storage medium 90 mentioned herein includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0168] It should also be noted that in this application, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0169] Those skilled in the art can further understand that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application. The principles and implementation of the present application are described by applying specific examples. The above description of the examples is only to help understand the technical solutions of the present application and its core ideas. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A method of diagnosing a fault of an electric machine, characterized by, The method comprises: detecting three-phase voltages and leakage currents of the motor; performing feature extraction based on the three-phase voltages and the leakage currents of the motor to obtain a feature extraction result; analyzing the feature extraction result to obtain a fault diagnosis result of the motor; wherein the types of the fault diagnosis result include: no fault of the motor, abnormal main insulation of the motor, abnormal turn-to-turn insulation of the motor, and abnormal bearing of the motor; performing feature extraction based on the three-phase voltages and the leakage currents of the motor to obtain a feature extraction result, comprising: performing frequency spectrum analysis on the leakage currents of the motor to obtain a leakage current frequency spectrum of the motor; taking the average value of the three-phase voltages of the motor as a common-mode voltage, and performing frequency spectrum analysis on the common-mode voltage to obtain a common-mode voltage frequency spectrum of the motor; based on the leakage current frequency spectrum of the motor, determining a resonance frequency within a first frequency range and a resonance point amplitude at the resonance frequency, and determining a resonance frequency within a second frequency range and a resonance point amplitude at the resonance frequency; based on the leakage current frequency spectrum and the common-mode voltage frequency spectrum of the motor, determining the common-mode voltage amplitude, the current amplitude, and the phase difference between the voltage and the current at the common-mode voltage fundamental frequency when the frequency is the common-mode voltage fundamental frequency; based on the determined common-mode voltage amplitude, current amplitude, and phase difference between voltage and current at the common-mode voltage fundamental frequency when the frequency is the common-mode voltage fundamental frequency, determining the main insulation equivalent capacitance value and the main insulation equivalent resistance value; the resonance frequency within the first frequency range and the resonance point amplitude at the resonance frequency, the resonance frequency within the second frequency range and the resonance point amplitude at the resonance frequency, the main insulation equivalent capacitance value and the main insulation equivalent resistance value are taken as the obtained feature extraction result.

2. The electric machine fault diagnostic method according to claim 1, characterized by, analyzing the feature extraction result to obtain a fault diagnosis result of the motor, comprising: inputting the feature extraction result into a trained neural network model to obtain the fault diagnosis result of the motor through the neural network model.

3. The motor fault diagnostic method according to claim 2, characterized by, detecting three-phase voltages and leakage currents of the motor, comprising: detecting the three-phase voltages and the leakage currents of the motor for a first time length each time the sampling is triggered; determining whether the working condition of the motor is stable within the first time length; if yes, performing the operation of performing feature extraction based on the three-phase voltages and the leakage currents of the motor to obtain a feature extraction result; if no, discarding the detected three-phase voltages and leakage currents of the motor for the first time length.

4. The motor fault diagnostic method according to claim 3, characterized by, determining whether the working condition of the motor is stable within the first time length, comprising: determining whether the phase voltage effective value and the voltage fundamental frequency of the motor remain stable within the first time length; if yes, determining that the working condition of the motor is stable, otherwise determining that the working condition of the motor is unstable.

5. The motor fault diagnostic method according to claim 3, characterized by, after determining that the working condition of the motor is stable, inputting the feature extraction result into a trained neural network model to obtain the fault diagnosis result of the motor through the neural network model, comprising: The feature extraction result is input into a trained neural network model corresponding to the current working condition, and a fault diagnosis result of the motor is obtained through the neural network model corresponding to the current working condition.

6. The motor fault diagnostic method according to claim 1, characterized by, Also comprising: Based on the leakage current of the motor, the time domain oscillation cycle mean and time domain oscillation cycle standard deviation of the leakage current, and the time domain peak to peak value mean and time domain peak to peak value standard deviation of the leakage current are determined, and are all added to the feature extraction result.

7. The motor fault diagnostic method according to claim 1, characterized by, Based on the determined common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude and the phase difference between the voltage and the current at the common mode voltage fundamental frequency, the main insulation equivalent capacitance value and the main insulation equivalent resistance value are determined, including: based on determining the main insulation equivalent capacitance value; based on determining the equivalent resistance value of the main insulation wherein C eq is the equivalent capacitance value of the main insulation, R eq is the equivalent resistance value of the main insulation, Z is the equivalent impedance of the main insulation, and Uc, Ic and θc are the common-mode voltage amplitude, the current amplitude and the phase difference between the voltage and the current, respectively, at a frequency of the common-mode voltage fundamental frequency, f c is the common-mode voltage fundamental frequency.

8. A motor fault diagnostic system characterized by comprising: Including: The sampling module is used for detecting the three-phase voltage and leakage current of the motor; The feature extraction module is used for feature extraction based on the three-phase voltage and leakage current of the motor, and a feature extraction result is obtained; The fault diagnosis module is used for analyzing the feature extraction result, and obtaining a fault diagnosis result of the motor; wherein the types of the fault diagnosis result include: motor fault-free, motor main insulation abnormal, motor turn-to-turn insulation abnormal, and motor bearing abnormal; The feature extraction module includes: The leakage current spectrum analysis unit is used for performing spectrum analysis on the leakage current of the motor, and obtaining the leakage current spectrum of the motor; The common mode voltage spectrum analysis unit is used for taking the average value of the three-phase voltage of the motor as the common mode voltage, and performing spectrum analysis on the common mode voltage, and obtaining the common mode voltage spectrum of the motor; The resonance point analysis unit is used for determining the resonance frequency in the first frequency range and the resonance point amplitude at the resonance frequency, and determining the resonance frequency in the second frequency range and the resonance point amplitude at the resonance frequency based on the leakage current spectrum of the motor; The main insulation impedance analysis unit is used for determining the common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude and the phase difference between the voltage and the current at the common mode voltage fundamental frequency based on the leakage current spectrum and the common mode voltage spectrum of the motor; and determining the main insulation equivalent capacitance value and the main insulation equivalent resistance value based on the determined common mode voltage amplitude corresponding to the frequency of the common mode voltage fundamental frequency, the current amplitude and the phase difference between the voltage and the current at the common mode voltage fundamental frequency. The feature extraction result determination unit is used for taking the resonance frequency in the first frequency range and the resonance point amplitude at the resonance frequency, the resonance frequency in the second frequency range and the resonance point amplitude at the resonance frequency, the main insulation equivalent capacitance value and the main insulation equivalent resistance value as the obtained feature extraction result.

9. The motor fault diagnostic system of claim 8, wherein The fault diagnosis module is specifically used for: The feature extraction result is input into a trained neural network model, and a fault diagnosis result of the motor is obtained through the neural network model.

10. The motor fault diagnostic system of claim 8, wherein The sampling module includes: The sampling unit is used for detecting the three-phase voltage and leakage current of the motor for a first time length every time the sampling is triggered; The working condition judgment unit is used for judging whether the working condition of the motor is stable within the first time length; If yes, triggering the feature extraction module; If no, triggering a discarding unit for discarding the detected three-phase voltage and leakage current of the motor in the first time length.

11. The motor fault diagnostic system of claim 8, wherein Further comprising a time domain analysis unit for: Based on the leakage current of the motor, determining the time domain oscillation cycle mean and time domain oscillation cycle standard deviation of the leakage current, and the time domain peak-peak value mean and time domain peak-peak value standard deviation of the leakage current, and adding them to the feature extraction result.

12. An electric machine failure diagnosing apparatus characterized by comprising: Comprise: A memory for storing a computer program; A processor for executing the computer program to realize the steps of the motor fault diagnosis method according to any one of claims 1 to 7.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium has a computer program stored thereon, and the computer program is executed by the processor to realize the steps of the motor fault diagnosis method according to any one of claims 1 to 7.

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