Online Diagnosis Method and System for Bearing Faults of Non-Invasive Permanent Magnet Synchronous Servo Motors

Abstract

The present invention discloses a non-invasive online diagnosis method and system for permanent magnet synchronous servo motor bearing faults, including: in the tuning stage, injecting a chirp current signal, calculating the frequency response function in combination with the rotational speed feedback signal, and determining the resonance frequency according to the frequency response function; calculating the theoretical characteristic order according to the motor bearing size parameters; in the motor operation stage, collecting the motor rotational speed signal and determining the theoretical characteristic frequency according to the theoretical characteristic order; determining the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, and performing band-pass filtering on the motor rotational speed signal according to the optimal resonance demodulation frequency band; calculating the envelope signal of the filtered signal online through the quadrature demodulation algorithm, performing FFT analysis on the spectrum of the envelope signal, and comparing the spectrum of the envelope signal with the amplitude corresponding to the theoretical characteristic frequency to complete the determination of the fault diagnosis result. The present invention realizes the problem of online diagnosis of permanent magnet synchronous servo motor bearing faults under the condition of low-performance chip resources.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and particularly to a non-invasive online diagnosis method and system for bearing faults of a permanent magnet synchronous servo motor. Background Art

[0002] In recent years, due to advantages such as high power density and efficiency, high torque-to-volume ratio, and excellent dynamic performance, permanent magnet synchronous servo motors have been widely used in the fields of industrial automation, new energy vehicles, industrial robots, etc. However, when they operate for a long time under harsh conditions, various reliability problems will inevitably occur in their key components. As one of the key mechanical components of a permanent magnet synchronous servo motor, the motor bearing has a high probability of failure. Therefore, it is of great significance to conduct online diagnosis and monitoring of the motor bearing.

[0003] The vibration signal analysis method is one of the most commonly used bearing fault diagnosis methods in the industrial field. This method requires installing additional sensors to collect signals, resulting in a relatively high diagnostic cost. On the other hand, to implement bearing fault diagnosis based on the vibration signal analysis method, it is necessary to accurately identify the optimal resonance demodulation frequency band. The identification of the optimal resonance demodulation frequency band usually requires equipment such as a hammering device or an exciter to participate, and the identification process is complex. Although the identification of the optimal resonance demodulation frequency band can also be achieved through algorithms such as the fast kurtosis spectrum, the implementation of advanced signal processing methods requires high chip computing power, which is not conducive to implementation in an embedded system. Currently, some scholars also conduct motor bearing fault diagnosis through motor current signal analysis, but the sensitivity of the current signal is relatively low, and it is usually only obvious when the fault degree is relatively serious. To meet the needs of high-performance motion control, a permanent magnet synchronous servo motor is equipped with a high-precision encoder. Although some scholars have currently carried out relevant research based on the speed signal of the servo motor, the identification of the optimal resonance demodulation frequency band mainly relies on complex signal processing methods, and the existing diagnostic solutions are difficult to deploy under the condition of low-performance chip resources. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Based on the above problems, the present invention provides a non-invasive online diagnosis method and system for bearing faults of a permanent magnet synchronous servo motor, so as to solve the problem that it is difficult to conduct online diagnosis of bearing faults of a permanent magnet synchronous servo motor under the condition of low-performance chip resources.

[0006] (2) Technical Solutions

[0007] Based on the above technical problems, the present invention provides a non-invasive online diagnosis method for bearing faults of a permanent magnet synchronous servo motor, including the following steps:

[0008] S1. Configure the calculation parameters of the diagnostic algorithm in the tuning stage:

[0009] S11. During the tuning stage of the frequency converter, a chirp current signal is injected into the quadrature axis of the motor by the frequency converter, and the frequency response function is calculated by combining the rotational speed feedback signal, and the resonance frequency is determined according to the frequency response function;

[0010] S12. Calculate the theoretical characteristic order according to the motor bearing size parameters;

[0011] S13. Record the resonance frequency and the theoretical characteristic order in the driver;

[0012] S2. Run the online operation diagnosis algorithm during the operation stage:

[0013] S21. During the normal operation stage of the motor, collect the motor rotational speed signal, and determine the theoretical characteristic frequency according to the theoretical characteristic order;

[0014] S22. Determine the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, perform band-pass filtering on the motor rotational speed signal according to the optimal resonance demodulation frequency band to obtain the filtered rotational speed signal, and calculate the envelope signal of the filtered rotational speed signal online through the quadrature demodulation algorithm;

[0015] S23. Analyze the envelope signal through Fourier transform to obtain the spectrum of the envelope signal;

[0016] S24. Compare the spectrum of the envelope signal with the amplitude corresponding to the theoretical characteristic frequency to determine the fault diagnosis result.

[0017] Further, in S11, the injected chirp current signal is:

[0018]

[0019] The formula for calculating the frequency response function by combining the rotational speed feedback signal is:

[0020]

[0021] The calculation formula for determining the resonance frequency according to the frequency response function is:

[0022]

[0023] In the formula, I c (t) represents the chirp current signal, I N represents the rated current of the motor, f0 represents the injection start frequency, f1 represents the injection end frequency, t1 is the injection duration, t represents the current time, F{·} represents the Fourier transform of the signal, w i (t) represents the rotational speed feedback signal, H(ω) represents the frequency response function, f c represents the resonance frequency.

[0024] Further, in S12, the formula for calculating the theoretical characteristic order according to the motor bearing size parameters is as follows:

[0025]

[0026] In the formula, O BI 、O BO 、O BE 、O CA respectively represent the theoretical characteristic orders of the inner ring, outer ring, rolling elements, and cage faults of the bearing, D represents the cage diameter, d represents the rolling element diameter, and a represents the rolling element contact angle.

[0027] Further, in S21, the method for determining the theoretical characteristic frequency according to the theoretical characteristic order is as follows:

[0028]

[0029] In the formula, f BI 、f BO 、f BE 、f CA respectively represent the theoretical characteristic frequencies of the inner ring, outer ring, rolling elements, and cage faults of the bearing, and w ave is the average value of the rotational speed signals collected during the normal operation stage of the motor.

[0030] Further, in S22, the formula for determining the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency is as follows:

[0031]

[0032] In the formula, B w represents the determined optimal resonance demodulation frequency band;

[0033] The method for online calculating the envelope signal of the filtered rotational speed signal through the quadrature demodulation algorithm includes: multiplying the filtered rotational speed signal by a carrier signal cos(2πf c t) in the same phase and obtaining the in-phase component I(t) through a low-pass filter LPF; multiplying the filtered rotational speed signal by a carrier signal sin(2πf c t) in the quadrature phase and obtaining the quadrature component Q(t) through a low-pass filter LPF; calculating the envelope signal:

[0034]

[0035] Further, in S24, the fault diagnosis result determination method includes:

[0036] Record the frequencies in the spectrum of the envelope signal that are the same as the theoretical characteristic frequencies f BI 、fBO and f BE and f CA The corresponding characteristic amplitudes are A BI , A BO , A BE , A CA , respectively. For the four fault types of the outer ring, inner ring, rolling elements and cage, the corresponding alarm thresholds are CI1, CI2, CI3, and CI4. Then:

[0037] If the characteristic amplitude A BI > the alarm threshold CI1, it is determined as a fault of the inner ring of the bearing;

[0038] If the characteristic amplitude A BO > the alarm threshold CI2, it is determined as a fault of the outer ring of the bearing;

[0039] If the characteristic amplitude A BE > the alarm threshold CI3, it is determined as a fault of the rolling elements;

[0040] If the characteristic amplitude A CA > the alarm threshold CI4, it is determined as a fault of the cage;

[0041] If the amplitudes corresponding to the theoretical characteristic frequencies do not exceed the threshold, it is determined that there is no bearing fault.

[0042] Furthermore, the starting frequency f0 of the chirp current signal is 10 Hz, the injection termination frequency f1 is taken as one-fifth of the inverter switching frequency, and the injection duration is between 2 and 4 s.

[0043] The present invention also discloses a non-invasive online diagnosis system for permanent magnet synchronous servo motor bearing faults, including:

[0044] At least one processor; and at least one memory communicatively connected to the processor, wherein:

[0045] The memory stores program instructions executable by the processor, and the processor can execute the method by calling the program instructions.

[0046] The present invention also discloses a non-transitory computer-readable storage medium, and the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method.

[0047] (III) Advantageous Effects

[0048] The above technical solutions of the present invention have the following advantages:

[0049] (1) The present invention accurately identifies the resonance frequency by injecting a chirp current signal into the quadrature axis of the motor, determines the optimal resonance demodulation frequency band, and does not require a hammering device and advanced signal processing methods. By analyzing the motor speed signal, the envelope signal is obtained through band-pass filtering and quadrature demodulation, and then the spectrum of the envelope signal is obtained through Fourier transform. Finally, the amplitude corresponding to the spectrum of the envelope signal is compared with a threshold value to achieve online diagnosis of four types of faults. It does not require additional diagnostic sensors and additional MCU chips, and can rely on the computing power of the driver chip. The overall method is simple, reliable, low-cost, the diagnostic scheme can be implemented online, and the requirement for chip computing power is low;

[0050] (2) Through the online diagnosis of fault types, the present invention improves the reliability and safety of the operation of the servo motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings. The drawings are schematic and should not be construed as limiting the present invention in any way. In the drawings:

[0052] Figure 1 is a flowchart of a non-invasive online diagnosis method for bearing faults of a permanent magnet synchronous servo motor according to an embodiment of the present invention;

[0053] Figure 2 is a control schematic diagram of a frequency response function calculation method according to an embodiment of the present invention;

[0054] Figure 3 is a time-domain waveform diagram of the injected chirp current signal and the corresponding speed feedback signal according to an embodiment of the present invention;

[0055] Figure 4 is a waveform diagram of the frequency response function curve according to an embodiment of the present invention;

[0056] Figure 5 is a time-domain waveform diagram of the speed signal when the motor is operating normally according to an embodiment of the present invention;

[0057] Figure 6 is a schematic diagram of the calculation method of the envelope signal of the speed signal according to an embodiment of the present invention;

[0058] Figure 7 is an FFT spectrum waveform diagram of the envelope signal according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The following will further describe in detail the specific embodiments of the present invention in conjunction with the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0060] An embodiment of the present invention is a non-invasive online diagnosis method for bearing faults of a permanent magnet synchronous servo motor. This method makes full use of the signal sources and computing power resources of the driver itself. During the frequency conversion tuning stage, the frequency response function is calculated by the chirp current signal injection method to obtain the resonance frequency, and the optimal resonance demodulation frequency band is jointly determined in combination with the theoretical characteristic frequency of bearing faults. During the normal operation stage of the motor, the motor speed signal is band-pass filtered based on the determined optimal resonance demodulation frequency band, and the envelope of the filtered signal is calculated online through the quadrature demodulation algorithm. Finally, the envelope spectrum is calculated by FFT and compared with the amplitude corresponding to the theoretical characteristic frequency to determine the fault diagnosis result. This method has high economic value for improving the operation reliability of the servo drive system. As Figure 1 shown, the specific steps are as follows:

[0061] S1. Tuning stage - Configure the calculation parameters of the diagnostic algorithm: During the motor tuning stage, the resonance frequency is obtained by calculating the frequency response function, the theoretical characteristic order of bearing faults is calculated according to the bearing size parameters, and the resonance frequency and the theoretical characteristic order are recorded in the driver.

[0062] S11. During the frequency conversion tuning stage, use the frequency converter to inject a chirp current signal into the quadrature axis of the motor, calculate the frequency response function in combination with the speed feedback signal, and determine the resonance frequency according to the frequency response function;

[0063] Figure 2 is the control schematic diagram for calculating the frequency response function. Use the frequency converter to inject a chirp current signal into the quadrature axis of the motor and collect the corresponding speed feedback signal to calculate the frequency response function. The injected chirp current signal can specifically be:

[0064]

[0065] where I c (t) represents the chirp current signal, I N represents the rated current of the motor, f0 represents the injection start frequency, f1 represents the injection end frequency, t1 is the injection duration, and t represents the current time. Among them, the injection start frequency f0 of the chirp current signal is usually taken as 10Hz, the injection end frequency f1 is usually taken as one-fifth of the inverter switching frequency, and the injection duration is usually about 2 - 4s.

[0066] Figure 3 is the time-domain waveform diagram of the injected chirp current signal and the corresponding speed feedback signal. Figure 4 is the time-domain waveform diagram of the frequency response function curve H(ω). The formula for calculating the frequency response function in combination with the speed feedback signal can specifically be:

[0067]

[0068] where F{·} represents the Fourier transform of the signal, and w i (t) represents the rotational speed feedback signal, and H(ω) represents the frequency response function.

[0069] The steps to determine the resonance frequency according to the frequency response function can specifically be:

[0070]

[0071] where f c represents the resonance frequency. Figure 4 The frequency corresponding to the point with the highest amplitude in c is the resonance frequency f

[0072] S12. Calculate the theoretical characteristic order according to the motor bearing size parameters;

[0073] The specific steps to calculate the theoretical characteristic order according to the motor bearing size parameters can be:

[0074]

[0075] where O BI , O BO , O BE , O CA respectively represent the theoretical characteristic orders of the inner ring, outer ring, ball, and cage faults of the bearing, D represents the cage diameter, d represents the ball diameter, and a represents the ball contact angle. Information such as the size parameters of the bearing can be obtained by surveying before the motor leaves the factory, or can be obtained by querying the database according to the bearing model.

[0076] S13. Record the resonance frequency and the theoretical characteristic order in the driver.

[0077] Record parameters such as the resonance frequency and the theoretical characteristic order in the driver to complete the configuration of the parameters related to the tuning stage and the diagnostic algorithm.

[0078] When the tuning is successful, enter the normal operation stage of the motor; otherwise, adjust the parameters until the tuning is successful.

[0079] S2. Operation stage - Online operation diagnostic algorithm: In the normal operation stage of the motor, determine the optimal resonance demodulation frequency band, use the quadrature demodulation algorithm to calculate the envelope of the signal after band-pass filtering online, and use FFT to calculate the envelope spectrum and compare the amplitude corresponding to the theoretical characteristic frequency to determine the fault diagnosis result.

[0080] S21. In the normal operation stage of the motor, collect the motor rotational speed signal and determine the theoretical characteristic frequency according to the theoretical characteristic order;

[0081] Figure 5 is the time-domain waveform of the rotational speed signal collected in the normal operation stage of the motor. Denote wave is the average value of the rotational speed signal during the sampling duration. The specific steps to determine the theoretical characteristic frequency by combining the theoretical characteristic order recorded in the drive during the tuning phase can be as follows:

[0082]

[0083] where f BI , f BO , f BE , f CA respectively represent the theoretical characteristic frequencies of the inner ring, outer ring, balls, and cage faults of the bearing, and w ave is the average value of the rotational speed signal collected during the normal operation phase of the motor.

[0084] S22. Determine the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, perform band-pass filtering on the motor rotational speed signal according to the optimal resonance demodulation frequency band to obtain the filtered rotational speed signal, and calculate the envelope signal of the filtered rotational speed signal online through the quadrature demodulation algorithm;

[0085] Figure 6 is the schematic diagram of calculating the envelope signal by the quadrature demodulation algorithm. First, filter through the band-pass filter BPF, and then calculate the envelope signal of the filtered signal online through the quadrature demodulation algorithm.

[0086] Among them, the specific steps to determine the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency can be as follows:

[0087]

[0088] where B w represents the determined optimal resonance demodulation frequency band. The frequency range of the band-pass filter filtering can be determined according to the optimal demodulation frequency band.

[0089] The steps of calculating the envelope signal of the filtered rotational speed signal online through the quadrature demodulation algorithm include:

[0090] Multiply the filtered rotational speed signal by the carrier signal cos(2πf c t) with the same phase, and obtain the in-phase component I(t) through the low-pass filter LPF;

[0091] Multiply the filtered rotational speed signal by the carrier sin(2πf c t) with the quadrature phase, and obtain the quadrature component Q(t) through the low-pass filter LPF;

[0092] The steps of calculating the envelope signal can be as follows:

[0093]

[0094] S23. Analyze the envelope signal through Fourier transform (FFT) to obtain the spectrum of the envelope signal; Figure 7 It is the FFT spectrum waveform diagram of the envelope signal.

[0095] S24. Compare the spectrum of the envelope signal with the amplitude corresponding to the theoretical characteristic frequency to determine the fault diagnosis result.

[0096] Denote the characteristic amplitudes corresponding to the theoretical characteristic frequencies f BI 、f BO 、f BE 、f CA in the spectrum of the envelope signal as A BI 、A BO 、A BE 、A CA . The alarm thresholds corresponding to the four fault types of the outer ring, inner ring, rolling elements, and cage are CI1, CI2, CI3, and CI4 respectively. By comparing the characteristic amplitudes with the corresponding alarm thresholds, the fault diagnosis result is determined. The specific determination rules are as follows:

[0097] If the characteristic amplitude A BI > alarm threshold CI1, it is judged as a bearing inner ring fault;

[0098] If the characteristic amplitude A BO > alarm threshold CI2, it is judged as a bearing outer ring fault;

[0099] If the characteristic amplitude A BE > alarm threshold CI3, it is judged as a rolling element fault;

[0100] If the characteristic amplitude A CA > alarm threshold CI4, it is judged as a cage fault;

[0101] If the amplitudes corresponding to the theoretical characteristic frequencies do not exceed the threshold, it is judged that there is no bearing fault.

[0102] The fault alarm threshold can also be set according to the test values of healthy motors under the same diagnostic algorithm.

[0103] Finally, it should be noted that the above diagnostic method can be converted into software program instructions, which can be implemented by running a diagnostic system including a processor and a memory, or can be implemented by computer instructions stored in a non-transitory computer-readable storage medium. The integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, external hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0104] In summary, through the above non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis method and system, the following beneficial effects are obtained:

[0105] (1) In the present invention, by injecting a chirp current signal into the quadrature axis of the motor, the resonance frequency is accurately identified, and the optimal resonance demodulation frequency band is determined. Without a hammering device and advanced signal processing methods, by analyzing the motor speed signal, the envelope signal is obtained through band-pass filtering and quadrature demodulation, and then the spectrum of the envelope signal is obtained through Fourier transform. Finally, the amplitude corresponding to the spectrum of the envelope signal is compared with a threshold value to achieve online diagnosis of 4 fault types. Without additional diagnostic sensors and additional MCU chips, relying on the chip computing power of the driver is sufficient. The overall method is simple and reliable, with low cost. The diagnostic scheme can be implemented online and has low requirements for chip computing power;

[0106] (2) Through the online diagnosis of fault types in the present invention, the reliability and safety of the servo motor operation are improved.

[0107] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the embodiments of the present invention are described in conjunction with the drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An online diagnosis method for bearing faults of a non-intrusive permanent magnet synchronous servo motor, characterized in that, It includes the following steps: S1. Configure the calculation parameters of the diagnostic algorithm in the tuning stage: S11. In the tuning stage of the frequency converter, use the frequency converter to inject a chirp current signal into the quadrature axis of the motor, calculate the frequency response function by combining the rotational speed feedback signal, and determine the resonance frequency according to the frequency response function; S12. Calculate the theoretical fault characteristic order according to the motor bearing size parameters; S13. Record the resonance frequency and the theoretical fault characteristic order in the driver; S2. Run the online diagnostic algorithm in the operation stage: S21. In the normal operation stage of the motor, collect the motor rotational speed signal, and determine the theoretical fault characteristic frequency according to the theoretical fault characteristic order; S22. Determine the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical fault characteristic frequency, perform band-pass filtering on the motor rotational speed signal according to the optimal resonance demodulation frequency band to obtain the filtered rotational speed signal, and online calculate the envelope signal of the filtered rotational speed signal through the quadrature demodulation algorithm; S23. Analyze the envelope signal through Fourier transform to obtain the spectrum of the envelope signal; S24. Compare the spectrum of the envelope signal with the amplitude corresponding to the theoretical fault characteristic frequency to determine the fault diagnosis result.

2. The on-line diagnosis method for bearing faults of a non-intrusive permanent magnet synchronous servo motor according to claim 1, characterized in that In S11, the injected chirp current signal is: The formula for calculating the frequency response function by combining the rotational speed feedback signal is: The calculation formula for determining the resonance frequency according to the frequency response function is: Where, I c (t) represents the chirp current signal, I N represents the rated current of the motor, f0 represents the starting injection frequency, f1 represents the ending injection frequency, t1 is the injection duration, t represents the current time, F{·} represents the Fourier transform of the signal, w i (t) represents the rotational speed feedback signal, H(ω) represents the frequency response function, f c represents the resonance frequency.

3. The online diagnosis method for non-invasive permanent magnet synchronous servo motor bearing faults according to claim 2, characterized in that In S12, the formula for calculating the theoretical fault characteristic order according to the motor bearing size parameters is: Wherein, O BI , O BO , O BE , O CA respectively represent the theoretical characteristic orders of the inner ring, outer ring, rolling elements and cage faults of the bearing, D represents the cage diameter, d represents the rolling element diameter, and a represents the rolling element contact angle.

4. The online diagnosis method for bearing faults of a non-invasive permanent magnet synchronous servo motor according to claim 3, characterized in that, In S21, the method for determining the theoretical fault characteristic frequency according to the theoretical fault characteristic order is: where f BI , f BO , f BE , f CA respectively represent the theoretical characteristic frequencies of the inner ring, outer ring, ball and cage faults of the bearing, and w ave is the average value of the rotational speed signals collected during the normal operation stage of the motor.

5. The online diagnosis method for bearing faults of a non-intrusive permanent magnet synchronous servo motor according to claim 4, characterized in that, In S22, the formula for determining the optimal resonance demodulation frequency band according to the resonance frequency and the theoretical fault characteristic frequency is: where B w represents the determined optimal resonance demodulation frequency band; The method for online calculating the envelope signal of the filtered rotational speed signal through the quadrature demodulation algorithm includes: multiplying the filtered rotational speed signal by a carrier signal cos(2πf c t) in the same phase, and obtaining the in-phase component I(t) through a low-pass filter LPF; multiplying the filtered rotational speed signal by a carrier signal sin(2πf c t) in the quadrature phase, and obtaining the quadrature component Q(t) through a low-pass filter LPF; calculating to obtain the envelope signal:

6. The online diagnosis method for bearing faults of a non-invasive permanent magnet synchronous servo motor according to claim 5, characterized in that In S24, the method for determining the fault diagnosis result includes: Denote that the characteristic amplitudes corresponding to the fault theoretical characteristic frequencies f BI 、f BO 、f BE 、f CA in the spectrum of the envelope signal are A BI 、A BO 、A BE 、A CA , respectively. The alarm thresholds corresponding to the four fault types of the outer ring, inner ring, rolling element and cage are CI1, CI2, CI3, CI4, respectively. Then: If the characteristic amplitude A BI > the alarm threshold CI1, it is determined that there is a fault in the inner ring of the bearing; If the characteristic amplitude A BO > the alarm threshold CI2, it is determined as a fault in the outer ring of the bearing; If the characteristic amplitude A BE > the alarm threshold CI3, it is determined as a rolling element fault; If the characteristic amplitude A CA > the alarm threshold CI4, it is judged as a cage failure; If the amplitudes corresponding to the theoretical fault characteristic frequencies do not exceed the threshold, it is judged that there is no bearing fault.

7. The online diagnosis method for bearing faults of a non-intrusive permanent magnet synchronous servo motor according to claim 2, wherein The starting frequency f0 of the chirp current signal is 10 Hz, the injection termination frequency f1 is taken as one-fifth of the inverter switching frequency, and the injection duration is between 2 and 4 s.

8. An online diagnosis system for bearing faults of a non-intrusive permanent magnet synchronous servo motor, characterized in that, It includes: At least one processor; And at least one memory communicatively connected to the processor, wherein: The memory stores program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 7 by calling the program instructions.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method according to any one of claims 1 to 7.

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