Three-phase asynchronous motor fault diagnosis method and device and storage medium

Abstract

The invention provides a three-phase asynchronous motor fault diagnosis method and device and a storage medium, and belongs to the field of motor fault diagnos.The diagnosis method comprises the steps that motor state signals of a to-be-detected motor are obtained, and the motor state signals comprise a three-phase current signal, a stator current signal and a vibration current signal; performing sliding discrete Fourier transform on the motor state signal to obtain a spectrogram at each sampling moment; and performing spectral analysis on the spectrogram at each sampling moment, and determining the fault type of the motor. And scaling the spectrogram according to a specified frequency range and a specified sample number to obtain a spectrogram in a specified interval. Through the method provided by the invention, various common motor faults such as a coupling eccentric fault, a rotor fracture fault, a three-phase imbalance fault, a bearing inner ring fault, a bearing outer ring fault and a bearing rolling body fault can be judged and pre-warned, and the problems of missing detection, misjudgment, low reliability and the like existing in a conventional diagnosis method are solved.

Description

Technical Field

[0001] The present invention relates to the field of motor fault diagnosis, and in particular to a three-phase asynchronous motor fault diagnosis method, an electronic device and a computer-readable storage medium. Background Art

[0002] With the continuous development of electrification production, large and medium-sized AC asynchronous motors have played an important role in human production, life and various fields, and have become an important driving force for industrial and agricultural production. Therefore, the correct operation of three-phase asynchronous motors is the most fundamental guarantee for achieving safe production. However, due to the long-term operation of three-phase asynchronous motors, it is inevitable that they will be damaged by human or natural factors, resulting in failures. According to the data of the power supply company, core short circuit, core loosening, winding insulation failure and distance short circuit failure are the most common faults of motor stators; on the rotor, the frequent faults include broken wires, broken end rings, rotor eccentricity, etc. In addition, the imbalance of power will also cause the motor to heat up and the insulator to age. If the situation is more serious, it will also cause the motor to burn out. When the motor fails, it will inevitably bring safety hazards, noise pollution, and low production efficiency and other negative effects. Therefore, how to effectively diagnose the faults of three-phase asynchronous motors is a hot issue in the current research on induction motors. The accuracy of motor fault diagnosis will directly affect its diagnostic effect, that is, the quality of its modeling will be the guarantee of its effective application.

[0003] The coupling of multiple links in the operation of the motor leads to the diversity of its fault causes and characteristics, which makes it difficult to diagnose the motor fault. The existing motor fault diagnosis technology is mainly based on a single parameter, which has fault characteristics such as armature current, rotor speed, vibration frequency, surface temperature, etc. Using a single indicator for motor fault diagnosis has problems such as missed detection, misjudgment, and low reliability. Summary of the invention

[0004] In view of the technical problem that the motor fault diagnosis method in the prior art can only determine a single type of motor fault, the present invention provides a three-phase asynchronous motor fault diagnosis method, which can identify multiple types of motor faults and reduce the misjudgment rate and missed detection rate.

[0005] To achieve the above-mentioned purpose, the first aspect of the present invention provides a three-phase asynchronous motor fault diagnosis method, and the fault diagnosis includes the following steps: obtaining a motor state signal of the motor to be tested, wherein the motor state signal includes a three-phase current signal, a stator current signal and a vibration current signal; performing a sliding discrete Fourier transform on the motor state signal to obtain a spectrum diagram at each sampling moment; performing a spectrum analysis on the spectrum diagram at each sampling moment to determine the type of motor fault.

[0006] Among them, the motor fault types include coupling eccentricity, rotor breakage, three-phase imbalance, bearing inner ring fault, bearing outer ring fault and bearing rolling element fault.

[0007] Further, in an exemplary embodiment of the present invention, the calculation formula of the sliding discrete Fourier transform may be: ;in, X a [ k ]yes t = a Discrete Fourier transform formula for moment calculation; X a-1 [ k ]yes t =( a -1) Discrete Fourier transform formula for moment calculation; T e is the collection time; k It is k frequency points; B 2 is the number of samples in the sliding interval; j is an imaginary unit; b =0, 1, 2... B -1; k =0, 1, 2... N -1.

[0008] Furthermore, in an exemplary embodiment of the present invention, the fault diagnosis method further includes: scaling the frequency spectrum by specifying a frequency range and a specified number of samples to obtain a frequency spectrum of a specified interval.

[0009] Furthermore, in an exemplary embodiment of the present invention, the spectrum analysis of the spectrum graph at each sampling moment to determine the type of motor fault may include: performing preliminary analysis on the spectrum graph at each sampling moment to distinguish between a normal spectrum graph and a fault spectrum graph; identifying fault features in the fault spectrum graph to determine the type of motor fault; the normal spectrum graph includes a normal time domain graph and a normal frequency domain graph, and the fault spectrum graph includes a fault time domain graph and a fault frequency domain graph.

[0010] Furthermore, in an exemplary embodiment of the present invention, the identifying of fault features in the fault spectrum diagram and determining the type of motor fault may include: determining the main fault harmonics of the three-phase current signal based on the harmonic jitter amplitude in the fault frequency domain diagram; when it is determined that there is at least one phase with a decrease in frequency domain amplitude and at least one phase with an increase in frequency domain amplitude in the main fault harmonics, judging that the motor fault type is a coupling eccentricity fault.

[0011] Furthermore, in an exemplary embodiment of the present invention, the identifying of fault features in the fault spectrum diagram and determining the type of motor fault may also include: for the stator current signal, comparing the degree of fluctuation of each harmonic in the fault frequency domain diagram with that of each harmonic in the normal frequency domain diagram; when it is determined that the degree of fluctuation of each harmonic is inconsistent, judging the type of motor fault to be three-phase voltage imbalance, and determining the harmonic with a large degree of fluctuation as the main fault harmonic of the stator current signal.

[0012] Further, in an exemplary embodiment of the present invention, with respect to the stator current signal, when it is determined that there is a rotor fault frequency feature in the fault frequency domain diagram, it can be determined that the motor fault type is a rotor break.

[0013] Further, in an exemplary embodiment of the present invention, the identification of fault features in the fault spectrum diagram and determination of the motor fault type may also include: for the vibration current signal, when it is determined that the fault time domain diagram of the vibration current signal is a probability density curve, and the probability density curve is skewed or dispersed, a preliminary judgment is made that there is a faulty bearing; the fault frequency domain diagram of the vibration current signal is divided into multiple frequency bands, and an analysis frequency band is determined; when it is determined that there is a bearing inner ring fault frequency feature in the analysis frequency band, the motor fault type is judged to be a bearing inner ring fault; when it is determined that there is a bearing outer ring fault frequency feature in the analysis frequency band, the motor fault type is judged to be a bearing outer ring fault; when it is determined that there is a rolling element fault frequency feature in the analysis frequency band, the motor fault type is judged to be a bearing rolling element fault.

[0014] A second aspect of the present invention provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by one or more of the above processors to enable the computer to implement the above-mentioned three-phase asynchronous motor fault diagnosis method.

[0015] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one program code, and the program code is loaded and executed by a processor to enable a computer to implement the above-mentioned three-phase asynchronous motor fault diagnosis method.

[0016] Through the technical solution provided by the present invention, the present invention has at least the following technical effects: (1) The motor fault diagnosis method of the present invention can identify various types of motor faults, thus solving the problems of missed detection, misjudgment, low reliability, etc. when a single indicator is used to diagnose motor faults in the prior art; (2) The sliding discrete Fourier transform adopted by the present invention can realize the calculation of the result once for each new sampling data, and has high real-time performance; (3) The present invention achieves observation of part of the spectrum with a zooming effect by limiting the frequency range of the sliding discrete Fourier transform, which is beneficial to the spectrum analysis of the faulty motor and is very effective for fault diagnosis.

[0017] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 A flow chart of a motor fault diagnosis method provided by the first embodiment of the present invention; Figure 2A A schematic diagram of a three-phase current input interface of a motor fault diagnosis device provided in a second embodiment of the present invention; Figure 2B A schematic diagram of a stator current input interface of a motor fault diagnosis device provided in a second embodiment of the present invention; Figure 3A A time domain diagram of a normal motor initial interval provided by the third embodiment of the present invention; Figure 3B A frequency domain diagram of the initial interval of a normal motor provided by the third embodiment of the present invention; Figure 4A A time domain diagram of the initial interval of a motor with coupling eccentricity fault provided by the third embodiment of the present invention; Figure 4B A frequency domain diagram of the initial interval of a motor with coupling eccentricity fault provided by the third embodiment of the present invention; Figure 5A A time domain diagram of a normal motor designated interval provided by a third embodiment of the present invention; Figure 5B A frequency domain diagram of a normal motor designated interval provided by the third embodiment of the present invention; Fig. 6A A time domain diagram of a specified interval of a motor with coupling eccentricity fault provided by the third embodiment of the present invention; Figure 6B A frequency domain diagram of a specified interval of a motor with coupling eccentricity fault provided by the third embodiment of the present invention; Fig. 7A A time domain diagram of a normal motor initial interval provided by a fourth embodiment of the present invention; Figure 7B A frequency domain diagram of the initial interval of a normal motor provided by the fourth embodiment of the present invention; Fig. 8AA time domain diagram of the initial interval of a motor with voltage imbalance fault provided by the fourth embodiment of the present invention; Figure 8B A frequency domain diagram of an initial interval of a motor with voltage imbalance fault provided by a fourth embodiment of the present invention; Fig.9A A time domain diagram of a normal motor designated interval provided by a fourth embodiment of the present invention; Fig. 9B A frequency domain diagram of a normal motor designated interval provided by a fourth embodiment of the present invention; Fig. 10A A time domain diagram of a designated interval of a motor with voltage imbalance fault provided by a fourth embodiment of the present invention; Fig. 10B A frequency domain diagram of a specified interval of a motor with voltage imbalance fault provided by a fourth embodiment of the present invention; Fig.11A A time domain diagram of a normal motor initial interval provided by a fifth embodiment of the present invention; Fig. 11B A frequency domain diagram of the initial interval of a normal motor provided by the fifth embodiment of the present invention; Fig. 12A A time domain diagram of the initial interval of a motor with a rotor fracture fault provided by the fifth embodiment of the present invention; Fig. 12B A frequency domain diagram of the initial interval of a motor with a rotor fracture fault provided by the fifth embodiment of the present invention; Fig.13A A time domain diagram of a normal motor designated interval provided by a fifth embodiment of the present invention; Fig. 13B A frequency domain diagram of a normal motor designated interval provided by a fifth embodiment of the present invention; Fig.14A A time domain diagram of a specified interval of a motor with a rotor fracture fault provided by a fifth embodiment of the present invention; Fig. 14B A frequency domain diagram of a specified interval of a motor with a rotor fracture fault provided by a fifth embodiment of the present invention; Fig.15A A time domain diagram of a normal motor initial interval provided by a sixth embodiment of the present invention; Fig. 15B A frequency domain diagram of the initial interval of a normal motor provided by the sixth embodiment of the present invention; Fig.16A A time domain diagram of the initial interval of a motor with a bearing inner race fault provided by the sixth embodiment of the present invention; Fig. 16B A frequency domain diagram of the initial interval of a motor with a bearing inner race fault provided by the sixth embodiment of the present invention; Fig.17A A time domain diagram of the initial interval of a motor with a bearing outer ring fault provided by the sixth embodiment of the present invention; Fig. 17B A frequency domain diagram of the initial interval of a motor with a bearing outer ring fault provided by the sixth embodiment of the present invention; Fig.18A A time domain diagram of the initial interval of a motor with a bearing rolling element failure provided by the sixth embodiment of the present invention; Fig.18B A frequency domain diagram of the initial interval of a motor with a bearing rolling element failure provided by the sixth embodiment of the present invention; Fig.19A A time domain diagram of a normal motor designated interval provided by a sixth embodiment of the present invention; Fig.19B A frequency domain diagram of a normal motor designated interval provided by a sixth embodiment of the present invention; Fig. 20A A time domain diagram of a motor with a bearing inner race fault in a specified interval provided by a sixth embodiment of the present invention; Fig. 20B A frequency domain diagram of a motor with a bearing inner race fault in a specified interval provided by a sixth embodiment of the present invention; Fig.21A A time domain diagram of a motor with a bearing outer ring fault in a specified interval provided by a sixth embodiment of the present invention; Fig. 21B A frequency domain diagram of a motor with a bearing outer ring fault in a specified interval provided by a sixth embodiment of the present invention; Fig.22A A time domain diagram of a motor with a bearing rolling element failure in a specified interval provided by a sixth embodiment of the present invention; Fig. 22B A frequency domain diagram of a specified interval of a motor with a bearing rolling element fault provided by the sixth embodiment of the present invention. DETAILED DESCRIPTION

[0019] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0020] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0021] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0022] Embodiment 1 Please refer to Figure 1 The first embodiment of the present invention provides a three-phase asynchronous motor fault diagnosis method, the method comprising the following steps: Step S101: Acquire the motor state signal of the motor to be tested, wherein the motor state signal includes a three-phase current signal, a stator current signal and a vibration current signal.

[0023] Step S102: performing a sliding discrete Fourier transform on the motor state signal to obtain a frequency spectrum at each sampling moment.

[0024] Furthermore, the spectrogram may be scaled by specifying a frequency range and a specified number of samples to obtain a spectrogram at a specified interval at each sampling time.

[0025] It should be noted that the specific construction process of the sliding discrete Fourier transform is as follows.

[0026] (1) Determine the spectrum resolution when constructing the spectrum analysis model .

[0027] Collection frequency , collection time , satisfying the spectrum resolution: (1) At t = At time, N samples x(an) The discrete Fourier transform (DFT) of the final sequence can be obtained by the following expression: (2) (3) In the formula, j is an imaginary unit; N represents the maximum number of samples, d and k are 0~(maximum number of samples-1), and B represents the number of samples in the selected sliding interval, that is, d= 0, 1, 2… N -1, k =0, 1, 2… N -1.

[0028] When the number of available samples is less than the required length N, the number of missing samples can be reduced to zero based on the zero-padding technique.

[0029] (2) For continuous signals, the sliding discrete Fourier transform (SDFT) is used. Based on a "sliding" algorithm, the calculation time must be shorter than the sampling time.

[0030] Therefore, the discrete Fourier formula can be transformed into a sliding discrete Fourier transform, and the expression of the sliding discrete Fourier transform is as follows: (4) In the formula, X a [ k ]yes t = a Discrete Fourier transform formula for moment calculation; X a-1 [ k ]yes t =( a -1) Discrete Fourier transform formula for moment calculation; T e is the collection time; k It is k frequency points; B is the number of samples in the sliding interval.

[0031] Known It was before t =( a -1) The discrete Fourier transform is calculated at each moment; the resulting SDFT expression means that the spectrum can be updated at each sampling time of the signal to be analyzed.

[0032] (3) Since the number of samples has been determined, when the sampling frequency is When the zoom effect is used, part of the spectrum can be observed. Scaling in SDFT will not cause special reasons, so that the number of samples observed in the diagram is reduced from B becomes , and the frequency range of the study can be limited to k , for the required frequency band Perform spectrum analysis.

[0033] Therefore, the expression for the sliding discrete Fourier transform of a specified interval is as follows: (5) in, j is an imaginary unit; b =0, 1, 2… -1; k =0, 1, 2… N -1.

[0034] (5) The spectrum resolution relationship is: Step S103: Perform spectrum analysis on the spectrum diagram at each sampling moment to determine the motor fault type, wherein the motor fault type may include coupling eccentricity, rotor fracture, three-phase imbalance, bearing inner ring fault, bearing outer ring fault and bearing rolling element fault.

[0035] Furthermore, in this embodiment, the process of performing spectrum analysis on the spectrum diagram at each sampling moment to determine the type of motor fault includes but is not limited to the following sub-steps S1031-S1032.

[0036] Step S1031: Perform a preliminary analysis on the spectrum graph at each sampling moment to distinguish between a normal spectrum graph and a fault spectrum graph, wherein the normal spectrum graph includes a normal time domain graph and a normal frequency domain graph; and the fault spectrum graph includes a fault time domain graph and a fault frequency domain graph.

[0037] Sub-step S1032: identifying fault features in the fault spectrum diagram and determining the motor fault type.

[0038] For example, if the motor state signal of the motor to be tested is a three-phase current signal, then the process of identifying the fault characteristics in the fault spectrum diagram and determining the eccentricity fault of the motor coupling can be as follows.

[0039] Sub-step S1032A1: Determine the main fault harmonics of the three-phase current signal based on the harmonic jitter amplitude in the fault frequency domain diagram.

[0040] Sub-step S1032A2: When it is determined that there is a decrease in the frequency domain amplitude of at least one phase and an increase in the frequency domain amplitude of at least one phase in the main fault harmonics, it is determined that the motor fault type is a coupling eccentricity fault.

[0041] Exemplarily, if the motor state signal of the motor to be tested is a stator current signal, the process of determining the motor voltage imbalance fault may be as follows.

[0042] Sub-step S1032B1: Compare the fluctuation degree of each harmonic in the fault frequency domain diagram with that of each harmonic in the normal frequency domain diagram.

[0043] Sub-step S1032B2: When it is determined that the fluctuation levels of the harmonics are inconsistent, the motor fault type is determined to be three-phase voltage imbalance, and the harmonic with a large fluctuation level is determined as the main fault harmonic of the stator current signal.

[0044] For example, if the motor state signal of the motor to be tested is a stator current signal, the process of determining whether the motor rotor is broken may be as follows.

[0045] Sub-step S1032C1: Compare the frequency amplitudes of the normal frequency domain diagram and the fault frequency domain diagram of the stator current signal.

[0046] Sub-step S1032C2: when it is determined that there is a rotor fault frequency feature in the fault frequency domain diagram, determine that the motor fault type is a rotor break.

[0047] For example, the rotor fault frequency characteristics can be confirmed through the fault frequency empirical formula, and the fault type can be obtained by combining the frequency domain diagram observation.

[0048] (1) Rotor faults mainly occur at the connection between the short-circuit bar and the end ring. By measuring the stator current value and analyzing its frequency signal, when a rotor fault occurs, the following signals will appear.

[0049] (7) in, is the rotor fault frequency; s is the slip rate; is the power frequency, c= 1, 3, 5…

[0050] (2) Other broken bar fault characteristics are: (8) in, is the frequency of other broken bar faults; s is the slip rate; is the power frequency; h is the harmonic order, h= 1, 3, 5 … .

[0051] If the vibration caused by the mechanical shaft speed is considered, the newly added frequency component for: (9) in, It is the frequency of other broken bar failures under the influence of the vibration caused by the mechanical shaft speed; c= 1, 3, 5 … ; h is the harmonic order, h= 1, 3, 5 … ; s is the slip rate; is the power supply frequency.

[0052] The current spectrum is also affected by high-frequency harmonics, especially those distributed near the rotor slot harmonics. The newly added frequency components , whose expression is: (10) in, is the other broken bar fault frequency considering the influence of high frequency harmonics; m= 1, 3, 5…; is the number of rotor bars; s is the slip rate; p is the number of pole pairs of the motor; λ is the leakage flux coefficient.

[0053] It should be noted that equations (8), (9) and (10) represent spectrum relations under three different conditions respectively.

[0054] (3) Obviously, the oscillation of load torque at different speeds will introduce different frequency components in the stator current spectrum. , whose expression is: (11) in, l= 1, 3, 5 …， is the number of rotor bars, is the rotation frequency. At the same time, this frequency also exists in misalignment faults , satisfying the above relationship, so these frequency lines cannot be detected without problems using conventional methods.

[0055] Exemplarily, if the motor state signal of the motor to be tested is a vibration current signal, the process of determining the motor bearing fault (including bearing inner race fault, bearing outer race fault and bearing rolling element fault) may be as follows.

[0056] Sub-step S1032D1: When it is determined that the fault time domain diagram of the vibration current signal is a probability density curve, and the probability density curve is skewed or dispersed, it is preliminarily determined that there is a faulty bearing.

[0057] It should be noted that the probability density method can be used to make a preliminary diagnosis of the fault time domain diagram of the vibration current signal. The time domain diagram of a fault-free bearing is a typical normal distribution curve, and the time domain diagram of a faulty bearing is a probability density curve, and the probability density curve may appear skewed or scattered.

[0058] Sub-step S1032D2: Divide the fault frequency domain diagram of the vibration current signal into multiple frequency bands, and determine the analysis frequency band.

[0059] Here, the above-mentioned frequency band interval refers to a plurality of frequency intervals into which the frequency domain diagram is divided according to different frequency ranges, and the above-mentioned analysis frequency band interval refers to an evaluation interval selected from the frequency band interval for performing fault analysis.

[0060] The low frequency band refers to the frequency range below 1kHz. The corresponding characteristic spectrum lines on the spectrum graph can be directly observed to make judgments. This frequency range is easily affected by mechanical and power supply interference, and the frequency components that reflect the fault in the early stage of the fault have very little energy in the low frequency band. Therefore, the signal-to-noise ratio is low and the fault detection sensitivity is poor.

[0061] The mid-frequency band refers to the frequency range of 1k to 20kHz. The peak value, RMS value or kurtosis index of the signal is used as the monitoring parameter. A bandpass filter is used to extract the resonant frequency components of bearing parts or structural parts, and the total signal power in the passband is used as the monitoring parameter.

[0062] The high frequency band refers to the frequency range of 20 to 80kHz. A large part of the impact energy caused by bearing failure is distributed in the high frequency band. Using suitable acceleration sensors and fixing methods to ensure a higher resonant frequency of the sensor, and using the resonance of the sensor or the resonance of the circuit to enhance the attenuated vibration current signal is very effective for fault diagnosis.

[0063] Sub-step S1032D3: When it is determined that there is a frequency feature of the inner race fault of the bearing in the analysis frequency band, the motor fault type is determined to be the inner race fault of the bearing. For the inner race fault of the bearing, the middle frequency band and the high frequency band can be determined as the analysis frequency band.

[0064] For example, the fault frequency characteristics of the inner ring of the bearing can be confirmed through the empirical formula of the fault frequency, and the fault type can be obtained by combining the frequency domain diagram.

[0065] The inner ring fault frequency is: (12) in, is the bearing inner ring fault frequency; is the rotation frequency; z is the number of rolling elements.

[0066] Sub-step S1032D4: when it is determined that there is a frequency characteristic of a bearing outer race fault in the analysis frequency band, the motor fault type is determined to be a bearing outer race fault.

[0067] For example, for bearing outer ring faults, the middle frequency band and the high frequency band can be determined as the analysis frequency band intervals.

[0068] For example, the fault frequency characteristics of the bearing outer ring can be confirmed through the fault frequency empirical formula, and the fault type can be obtained by combining the frequency domain diagram.

[0069] The outer race fault frequency is: (13) in, is the bearing outer ring fault frequency; is the rotation frequency; z is the number of rolling elements.

[0070] Sub-step S1032D5: When it is determined that there is a rolling element fault frequency feature in the analysis frequency band, the motor fault type is determined to be a bearing rolling element fault. For general rolling bearings, the low frequency band is 2 khz, the middle frequency band is 2 khz to 20 khz, and the high frequency band is the part greater than 20 khz. At this time, the fault frequency generally occurs in the low frequency band. Therefore, the low frequency band can be determined as the analysis frequency band interval.

[0071] For example, the fault frequency characteristics of the bearing rolling element can be confirmed through the fault frequency empirical formula, and the fault type can be obtained by combining the frequency domain diagram.

[0072] The rolling element failure frequency is: (14) (15) in, is the bearing rolling element failure frequency; is the rotation frequency; z is the number of rolling elements.

[0073] Moreover, the frequency conversion meets the following requirements: (16) (17) in, For frequency conversion; is the bearing inner ring fault frequency; is the bearing outer ring fault frequency; n is the motor speed.

[0074] In addition, the implementation environment of this embodiment includes at least one terminal and a server, and the method is executed on the terminal or the server respectively. The terminal and the server can be connected in communication to realize the interactive transmission of information.

[0075] Among them, the terminal can be any electronic product that can interact with the user through one or more methods such as keyboard, touchpad, touch screen, voice interaction, etc., such as PC (Personal Computer), PPC (Pocket Personal Computer), tablet computer, etc.

[0076] The server can be a single server or a server cluster composed of multiple servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0077] Embodiment 2 A second embodiment of the present invention provides a three-phase asynchronous motor fault diagnosis device, which includes a current signal acquisition module, a spectrum acquisition module and a spectrum analysis module.

[0078] The current signal acquisition module is used to acquire the motor state signal of the motor to be tested. The motor state signal includes a three-phase current signal, a stator current signal and a vibration current signal.

[0079] The spectrum acquisition module is used to perform sliding discrete Fourier transform on the motor state signal to obtain the spectrum at each sampling moment. The main function of the spectrum acquisition module is to use sliding discrete Fourier transform to construct a three-phase current signal conversion model and a single signal conversion model, thereby converting the three-phase current signal and a single signal (such as a stator current signal and a vibration current signal) into a spectrum consisting of a time domain graph and a frequency domain graph.

[0080] The spectrum analysis module is used to perform spectrum analysis on the spectrum graph at each sampling moment to determine the type of motor fault. In addition, the spectrum analysis module is also used to scale the spectrum graph by specifying a frequency range and a specified number of samples to obtain a spectrum graph of a specified interval.

[0081] Through the above modules, the fault diagnosis of the three-phase asynchronous motor can be realized. For example, the processing process of the motor fault diagnosis device for motor fault diagnosis is as follows.

[0082] Step S201: Acquire the motor status signal input from the main interface.

[0083] In the main interface, you can choose three-phase current signal or single signal input.

[0084] like Figure 2A and Figure 2B As shown, the main input interface of the motor fault diagnosis device can select to input three-phase current signals, stator current signals and vibration current signals.

[0085] Step S202: input the required features into the signal conversion model, and convert them into corresponding spectrograms through the spectrogram acquisition module.

[0086] The input data of the spectrum acquisition module include motor state signal, sample number, acquisition time, acquisition frequency, frequency domain interval range and actual sample interval, and the output data are time domain graph and frequency domain graph.

[0087] Step S203: The spectrum analysis module distinguishes the normal spectrum graph from the fault spectrum graph, and analyzes the fault spectrum graph to obtain the fault type.

[0088] The spectrum analysis module can judge and warn of many common faults such as coupling eccentricity fault, rotor fracture fault, three-phase imbalance fault, bearing inner ring fault, bearing outer ring fault, bearing rolling element fault, etc.

[0089] It should be noted that the above-mentioned device only uses the division of the above-mentioned functional modules as an example to illustrate when implementing its functions. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the method provided in the first embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0090] The second embodiment of the present invention also provides an electronic device, which includes a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by one or more of the above-mentioned processors so that the computer implements the three-phase asynchronous motor fault diagnosis method as described above.

[0091] Of course, the electronic device may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The electronic device may also include other components for realizing various functions of the device, which will not be described in detail here.

[0092] The second embodiment of the present invention further provides a computer-readable storage medium, in which at least one program code is stored. The program code is loaded and executed by a processor to enable a computer to implement the three-phase asynchronous motor fault diagnosis method as described above.

[0093] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical disc data storage device. Those skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc.

[0094] Embodiment 3 A third embodiment of the present invention provides a method for diagnosing a motor with a coupling eccentricity fault by using a three-phase asynchronous motor fault diagnosis device, comprising the following steps: Step S301: Collect three-phase current signals.

[0095] For example, the data can be collected through relevant collection equipment, and the three-phase current can be collected through the acs712 current sensor to send the relevant signal to ardunio, which is then transmitted to the computer to generate a txt file, and the txt file is imported into the fault diagnosis device.

[0096] Step S302: Select the three-phase signal input in the main interface, run the spectrum acquisition module in the fault diagnosis device, and obtain the specified interval spectrum of the faulty motor.

[0097] Here, the corresponding three-phase current signals (i.e., the collected signal phase a, the collected signal phase b, and the collected signal phase c), the number of samples, the collection time, the collection frequency, the frequency domain interval range, and the actual sample interval are taken as input, and the sliding discrete Fourier transform algorithm is used to output the specified interval time domain graph and the specified interval frequency domain graph.

[0098] Step S303: Click to start analysis, and determine the fault type by analyzing the time domain diagram and frequency domain diagram of the specified interval obtained by the spectrum analysis module.

[0099] In actual operation, the spectrum analysis module needs to combine the sliding discrete Fourier transform and motor fault-related knowledge to analyze the specified interval spectrum diagram of the faulty motor (including the specified interval time domain diagram and the specified interval frequency domain diagram).

[0100] Figure 3A Shown is the time domain diagram of the initial interval of a normal motor; Figure 3B Shown is the frequency domain diagram of the initial interval of a normal motor. Figure 4A The time domain diagram of the initial interval of the motor with coupling eccentricity fault is shown; Figure 4B The figure shows the frequency domain diagram of the initial interval of the motor with coupling eccentricity fault. Figure 3A and Figure 4A The red curve (the curve pointed by the letter a) is the a-phase current, the blue curve (the curve pointed by the letter b) is the b-phase current, and the green curve (the curve pointed by the letter c) is the c-phase current.

[0101] By comparison Figure 3A and Figure 4A It can be seen that in the initial interval time domain diagram, the curve of the fault-free motor (i.e., normal motor) is smooth, while the curve of the faulty motor jitters violently; Figure 3B and Figure 4BIt can be seen that the interval range of the initial interval frequency domain graph is too large, and it is impossible to see the obvious difference between the fault-free motor curve and the faulty motor curve, and thus it is impossible to determine the specific fault. It can be seen that although conventional signal processing methods (such as fast Fourier transform, discrete Fourier transform, etc.) can transform the collected motor signal from the time domain to the frequency domain for analysis, it is impossible to quickly and effectively determine the type of motor fault based on the obtained spectrum graph.

[0102] In order to further confirm the type of motor fault, the sliding discrete Fourier transform is used on the three-phase current signal, the interval of the frequency domain diagram is specified as 0~15Hz, and the values ​​of the 3rd harmonic, 5th harmonic and 7th harmonic are observed. Figure 5A Shown is the time domain diagram of a normal motor in a specified interval; Figure 5B Shown is the frequency domain diagram of a normal motor in a specified interval; Fig. 6A Shown is the time domain diagram of the specified interval of the motor with coupling eccentricity fault; Figure 6B Shown is the frequency domain diagram of the specified interval of the motor with coupling eccentricity fault.

[0103] By comparing the frequency domain diagram of the specified interval of the normal motor (i.e. Figure 5B ) and the specified interval frequency domain diagram of the motor with coupling eccentricity fault (i.e. Figure 6B ), it can be seen that: the 3rd, 5th, and 7th harmonic jitter amplitudes of the fault-free motor are small, almost no jitter; the 3rd, 5th, and 7th harmonic jitter amplitudes of the faulty motor are large, among which the 3rd harmonic jitter degree is the largest, which is the main fault harmonic; at the same time, the frequency domain amplitudes of phases a and c decrease, and the frequency domain amplitude of phase b increases. Based on the above fault characteristics, it can be determined that the motor fault type is a coupling eccentricity fault.

[0104] Embodiment 4 A fourth embodiment of the present invention provides a method for diagnosing a three-phase asynchronous motor fault by using a three-phase asynchronous motor fault diagnosis device, comprising the following steps: Step S401: Collect stator signals.

[0105] For example, the data can be collected through relevant collection equipment, the stator current can be collected through the ACS712 current sensor, and the relevant signal can be sent to Arduino, and then transmitted to the computer to generate a txt file, and the txt file can be imported into the fault diagnosis device.

[0106] Step S402: Select single signal input in the main interface, run the spectrum acquisition module in the fault diagnosis device, and obtain the specified interval spectrum of the faulty motor.

[0107] Here, the stator current signal, sample number, acquisition time, acquisition frequency, frequency domain interval range and actual sample interval are taken as input, and the sliding discrete Fourier transform algorithm is used to output the specified interval time domain diagram and the specified interval frequency domain diagram.

[0108] Step S403: Click to start analysis, and determine the fault type by analyzing the time domain diagram and the frequency domain diagram of the specified interval obtained by the spectrum analysis module.

[0109] Fig. 7A The figure shows the time domain diagram of the initial interval of a normal motor. Figure 7B Shown is the frequency domain diagram of the initial interval of a normal motor. Fig. 8A The figure shows the time domain diagram of the initial interval of the motor with voltage imbalance fault. Figure 8B Shown is the frequency domain diagram of the initial interval of the motor with voltage unbalance fault.

[0110] By comparing the spectrum of the normal motor and the faulty motor in the initial interval (that is, comparing Fig. 7A and Fig. 8A , and contrast Figure 7B and Figure 8B ), it can be seen that the difference between the normal motor curve and the faulty motor curve in the initial interval time domain diagram and the initial interval frequency domain diagram is not obvious, and the specific fault of the motor cannot be determined. It can be seen that although the conventional signal processing methods (such as fast Fourier transform, discrete Fourier transform, etc.) can transform the collected motor signal from the time domain to the frequency domain for analysis, it is impossible to quickly and effectively determine the type of motor fault based on the obtained spectrum diagram.

[0111] In order to further confirm the type of motor fault, the stator current signal is subjected to sliding discrete Fourier transform, and the frequency domain diagram is assigned an interval of 0~70 Hz. Fig.9A Shown is the time domain diagram of a normal motor in a specified interval; Fig. 9B Shown is the frequency domain diagram of a normal motor in the specified interval. Fig. 10A Shown is the time domain diagram of the specified interval of the motor with voltage imbalance fault; Fig. 10B Shown is the frequency domain diagram of the specified interval of the motor with voltage unbalance fault.

[0112] By comparing the frequency domain diagram of the specified interval of the normal motor (i.e. Fig. 9B ) and the specified interval frequency domain diagram of the voltage unbalance fault motor (i.e. Fig. 10B ) It can be seen that the stator current frequency is 10hz, of which area A is the 3rd harmonic, area B is the 5th harmonic, and area C is the 7th harmonic; the 3rd harmonic of the faulty motor is the main fault harmonic, and the fluctuation of the 3rd harmonic is greater than that of the normal motor; and the fluctuation of the 7th harmonic is smaller than that of the normal motor. Based on the above fault characteristics, it can be judged that the motor fault type is three-phase voltage imbalance.

[0113] Embodiment 5 A fifth embodiment of the present invention provides a method for diagnosing a motor with a rotor fracture fault by using a three-phase asynchronous motor fault diagnosis device, comprising the following steps: Step S501: Collect stator signals.

[0114] Step S502: Select single signal input in the main interface, run the spectrum acquisition module in the fault diagnosis device, and obtain the specified interval spectrum of the faulty motor.

[0115] Here, the stator current signal, sample number, acquisition time, acquisition frequency, frequency domain interval range and actual sample interval are taken as input, and the sliding discrete Fourier transform algorithm is used to output the specified interval time domain diagram and the specified interval frequency domain diagram.

[0116] Step S503: Click to start analysis, and determine the fault type by analyzing the time domain diagram and the frequency domain diagram of the specified interval obtained by the spectrum analysis module.

[0117] Fig.11A Shown is the time domain diagram of the initial interval of a normal motor; Fig. 11B Shown is the frequency domain diagram of the initial interval of a normal motor. Fig. 12A The time domain diagram of the initial interval of the motor with rotor fracture fault is shown; Fig. 12B Shown is the frequency domain diagram of the initial interval of the motor with rotor fracture fault.

[0118] By comparing the spectrum of the normal motor and the faulty motor in the initial interval (that is, comparing Fig.11A and Fig. 12A , and contrast Fig. 11B and Fig. 11B ), it can be seen that the difference between the normal motor curve and the faulty motor curve in the initial interval time domain diagram and the initial interval frequency domain diagram is not obvious, and the specific fault of the motor cannot be determined. It can be seen that although the conventional signal processing methods (such as fast Fourier transform, discrete Fourier transform, etc.) can transform the collected motor signal from the time domain to the frequency domain for analysis, it is impossible to quickly and effectively determine the type of motor fault based on the obtained spectrum diagram.

[0119] In order to further confirm the type of motor fault, the sliding discrete Fourier transform is used on the stator current signal, and the interval of the frequency domain diagram is specified as 0~80hz. Fig.13A Shown is the time domain diagram of a normal motor in a specified interval; Fig. 13B Shown is the frequency domain diagram of a normal motor in the specified interval. Fig.14A Shown is the time domain diagram of the specified interval of the motor with rotor fracture fault; Fig. 14B Shown is the frequency domain diagram of the specified interval of the motor with rotor fracture fault.

[0120] By comparing the frequency domain diagram of the specified interval of the normal motor (i.e. Fig. 13B ) and the frequency domain diagram of the specified interval of the rotor fracture fault motor (i.e. Fig. 14B) It can be seen that: the maximum peak value of the 3rd harmonic of a normal motor is 0.05, and the amplitudes of the 5th and 7th harmonics are small; the maximum peak value of the 3rd harmonic of a faulty motor is less than 0.04, and the amplitude of the 5th harmonic increases. Based on the above fault characteristics, it can be determined that the motor fault type is a broken rotor.

[0121] Embodiment 6 A sixth embodiment of the present invention provides a method for diagnosing a motor with a rotor fracture fault by using a three-phase asynchronous motor fault diagnosis device, comprising the following steps: Step S601: collecting vibration current signals.

[0122] For example, the data can be collected through relevant collection equipment, the vibration current signal can be collected through the vibration collection sensor, and the relevant signal can be sent to ardunio, and then transmitted to the computer to generate a txt file, and the txt file can be imported into the fault diagnosis device.

[0123] Step S602: Select single signal input in the main interface, run the spectrum acquisition module in the fault diagnosis device, and obtain the specified interval spectrum of the faulty motor.

[0124] Here, the vibration current signal, sample number, acquisition time, acquisition frequency, frequency domain interval range and actual sample interval are taken as input, and the sliding discrete Fourier transform algorithm is used to output the specified interval time domain graph and the specified interval frequency domain graph.

[0125] Step S603: Click to start analysis, and determine the fault type by analyzing the time domain diagram and the frequency domain diagram of the specified interval obtained by the spectrum analysis module.

[0126] The bearing fault model motor uses rolling bearings, the motor speed is 600r / min, and the rolling element is 8.

[0127] (1) Use the probability density diagnosis method to perform a rough diagnosis on the initial interval time domain diagram to confirm whether there is a faulty bearing.

[0128] Fig.15A Shown is the time domain diagram of the initial interval of a normal motor; Fig. 15B Shown is the frequency domain diagram of the initial interval of a normal motor. Fig.16A The figure shows the time domain diagram of the initial interval of the motor with bearing inner ring fault; Fig. 16B Shown is the frequency domain diagram of the initial interval of the motor with bearing inner ring fault. Fig.17A The figure shows the time domain diagram of the initial interval of the motor with bearing outer ring fault; Fig. 17B Shown is the frequency domain diagram of the initial interval of the motor with bearing outer ring failure. Fig.18A The figure shows the time domain diagram of the initial interval of the motor with bearing rolling element failure; Fig.18B Shown is the frequency domain diagram of the initial interval of the motor with bearing rolling element failure.

[0129] By comparing the time domain diagrams of the normal motor with the motor with inner ring fault, the motor with outer ring fault, and the motor with rolling element fault in the initial interval (that is, comparing Fig.15A and Fig.16A ,contrast Fig.15A and Fig.17A ,as well as Fig.15A and Fig.18A ), it can be seen that: the vibration current signal in the normal motor time domain diagram presents a typical normal distribution, while the vibration current signals in the bearing inner ring fault motor time domain diagram, the bearing outer ring fault motor time domain diagram, and the bearing rolling element fault motor time domain diagram are too inclined, indicating that there is a faulty bearing.

[0130] (2) Select and analyze the spectrum band of the initial interval frequency domain diagram to confirm whether it is a bearing inner or outer ring fault.

[0131] The frequency domain diagrams of the normal motor, the motor with inner ring fault of the bearing, the motor with outer ring fault of the bearing, and the motor with rolling element fault of the bearing in the initial interval are divided into three frequency regions: low frequency band (below 1kHz), medium frequency band (1k~20kHz) and high frequency band (20~80kHz).

[0132] By comparing the frequency domain diagrams of the normal motor with the motor with inner ring fault, the motor with outer ring fault, and the motor with rolling element fault in the initial interval (that is, comparing Fig. 15B and Fig. 16B ,contrast Fig. 15B and Fig. 17B ,as well as Fig. 15B and Fig.18B ), it can be seen that: compared with the normal motor frequency domain diagram, the vibration of the motor frequency domain diagram of the bearing inner ring fault and the motor frequency domain diagram of the bearing outer ring fault is not obvious in the low frequency band and the medium frequency band, while the vibration amplitude in the high frequency band is too large, which can be preliminarily confirmed that there is a fault in the inner and outer ring of the bearing.

[0133] (3) Analyze the frequency spectrum in the specified interval and finally confirm the type of motor fault.

[0134] The vibration current signal is subjected to sliding discrete Fourier transform, and the intervals are selected as the low-frequency band and the medium-frequency band, so that the spectrum diagram of the normal motor in the specified interval and the spectrum diagram of the faulty motor in the specified interval can be obtained.

[0135] Fig.19A Shown is the time domain diagram of a normal motor in a specified interval; Fig.19B Shown is the frequency domain diagram of a normal motor in the specified interval. Fig. 20A The figure shows the time domain diagram of the specified interval of the motor with the inner race of the bearing failure; Fig. 20B Shown is the frequency domain diagram of the specified interval of the motor with bearing inner race fault. Fig.21A The figure shows the time domain diagram of the specified interval of the motor with bearing outer ring fault; Fig. 21B Shown is the frequency domain diagram of the specified interval of the motor with bearing outer race fault. Fig.22A The figure shows the time domain diagram of the specified interval of the motor with bearing rolling element failure; Fig. 22B Shown is the frequency domain diagram of the specified interval of the motor with bearing rolling element failure.

[0136] By comparing the frequency domain diagrams of the normal motor and the motor with bearing inner ring fault in the specified interval (that is, comparing Fig.19B and Fig. 20B ), it can be seen that: Fig.19B The low-frequency vibration current signal of the normal motor is normal, the first peak of the two segments is twice that of the second segment, there is no other signal, and the amplitude of the mid-frequency signal is not too high; Fig.19B compared to, Fig. 20B The peak values ​​at both ends of the low-frequency band in the second section become smaller, and the amplitude of the medium-frequency band increases. Based on the above fault characteristics, it can be determined that the motor fault type is a bearing inner ring fault.

[0137] By comparing the frequency domain diagrams of the normal motor and the motor with bearing outer ring fault in the specified interval (that is, comparing Fig.19B and Fig. 21B ), it can be seen that: Fig.19B compared to, Fig. 21B The low-frequency peak shifts to the left, and the mid-frequency amplitude increases. Based on the above fault characteristics, it can be determined that the motor fault type is a bearing outer ring fault.

[0138] By comparing the frequency domain diagrams of the normal motor and the motor with bearing rolling element failure in the specified interval (that is, comparing Fig.19B and Fig. 22B ), it can be seen that: Fig.19B compared to, Fig. 22B The low-frequency peak value in the middle frequency band rises sharply, and the amplitude in the middle frequency band rises sharply. Based on the above fault characteristics, it can be judged that the motor fault type is a bearing rolling element fault.

[0139] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0140] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0141] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A three-phase asynchronous motor fault diagnosis method, characterized in that: The fault diagnosis method comprises: Acquire a motor state signal of the motor to be tested, wherein the motor state signal includes a three-phase current signal, a stator current signal and a vibration current signal; Perform sliding discrete Fourier transform on the motor state signal to obtain the spectrum diagram at each sampling moment; Perform spectrum analysis on the spectrum diagram at each sampling moment to determine the type of motor fault.

2. The three-phase asynchronous motor fault diagnosis method according to claim 1, characterized in that: The calculation formula of the sliding discrete Fourier transform is: ； in, X a [ k ]yes t = a Discrete Fourier transform formula for moment calculation; X a-1 [ k ]yes t =( a -1) Discrete Fourier transform formula for moment calculation; T e is the collection time; k It is k frequency points; B 2 is the number of samples in the sliding interval; j is the imaginary unit; b=0, 1, 2…B2-1, k=0, 1, 2…N-1.

3. The three-phase asynchronous motor fault diagnosis method according to claim 2, characterized in that: The fault diagnosis method further includes: scaling the frequency spectrum by specifying a frequency range and a specified number of samples to obtain a frequency spectrum of a specified interval.

4. The three-phase asynchronous motor fault diagnosis method according to claim 1, characterized in that: The spectrum analysis is performed on the spectrum diagram at each sampling moment to determine the motor fault type, including: Perform a preliminary analysis on the spectrum at each sampling moment to distinguish between normal spectrum and fault spectrum; Identify fault features in the fault spectrum diagram and determine the motor fault type; The normal spectrum diagram includes a normal time domain diagram and a normal frequency domain diagram, and the fault spectrum diagram includes a fault time domain diagram and a fault frequency domain diagram.

5. The three-phase asynchronous motor fault diagnosis method according to claim 4, characterized in that: The step of identifying the fault characteristics in the fault spectrum diagram and determining the motor fault type includes: Based on the harmonic jitter amplitude in the fault frequency domain diagram, determine the main fault harmonics of the three-phase current signal; When it is determined that there is a decrease in the frequency domain amplitude of at least one phase and an increase in the frequency domain amplitude of at least one phase in the main fault harmonics, it is determined that the motor fault type is a coupling eccentricity fault.

6. The three-phase asynchronous motor fault diagnosis method according to claim 4, characterized in that: The method of identifying the fault characteristics in the fault spectrum diagram and determining the motor fault type also includes: For the stator current signal, compare the fluctuation degree of each harmonic in the fault frequency domain diagram with that in the normal frequency domain diagram; When it is determined that the fluctuation levels of the harmonics are inconsistent, the motor fault type is determined to be three-phase voltage imbalance, and the harmonic with a large fluctuation level is determined to be the main fault harmonic of the stator current signal.

7. The three-phase asynchronous motor fault diagnosis method according to claim 6, characterized in that: For the stator current signal, when it is determined that there is a rotor fault frequency feature in the fault frequency domain diagram, the motor fault type is judged to be a rotor break.

8. The three-phase asynchronous motor fault diagnosis method according to claim 4, characterized in that: The identifying fault features in the fault spectrum diagram and determining the motor fault type also includes: For the vibration current signal, when it is determined that the fault time domain diagram of the vibration current signal is a probability density curve, and the probability density curve is skewed or dispersed, it is preliminarily determined that there is a faulty bearing; Divide the fault frequency domain diagram of the vibration current signal into multiple frequency bands, and determine the analysis frequency bands; When it is determined that there is a frequency characteristic of the inner race fault of the bearing in the analysis frequency band, it is determined that the motor fault type is the inner race fault of the bearing; When it is determined that there is a frequency characteristic of the bearing outer race fault in the analysis frequency band, it is determined that the motor fault type is a bearing outer race fault; When it is determined that there is a rolling element fault frequency feature in the analysis frequency band, the motor fault type is determined to be a bearing rolling element fault.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by one or more of the above processors so that the computer implements the three-phase asynchronous motor fault diagnosis method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the program code is loaded and executed by a processor so that a computer implements the three-phase asynchronous motor fault diagnosis method according to any one of claims 1 to 8.

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