Bearing fault diagnosis method and system for optimizing filtering MCKD

Abstract

The embodiment of the invention provides a bearing fault diagnosis method and system for optimizing filtering MCKD, and the method comprises the steps: collecting a vibration signal, and carrying out the Fourier transform of a frequency spectrum of the vibration signal, and obtaining a key function; setting an initial interception length, performing inverse Fourier transform on the key function based on the initial interception length to obtain a frequency spectrum trend, dividing filtering frequency bands with different bandwidths and center frequencies by taking a minimum value of a trend spectrum as a boundary of frequency spectrum segmentation, increasing the interception length, repeating the steps, and recording all results; designing a Mayer wavelet filter based on the obtained filtering frequency band, and taking a designed Mayer wavelet filter coefficient as an initial filter coefficient of MCKD; mCKD filtering results of different initial filter coefficients are calculated, the fault characteristics of all filtering signals are quantized through harmonic spectrum kurtosis HSK, the filtering result corresponding to the maximum HSK value is selected as the global optimal solution of MCKD, and fault diagnosis is completed.

Description

Technical Field

[0001] This document relates to the field of fault diagnosis technology, and in particular to a bearing fault diagnosis method and system for optimizing filtered MCKD. Background Art

[0002] Research shows that the collected signal is a mixture of multiple convolutions such as fault excitation sources, noise and transmission paths. Therefore, some scholars have proposed the concept of blind deconvolution, hoping to design an inverse filter to make the filtered signal approximate the fault source signal. In order to better measure fault information, the existing Maximum Correlated Kurtosis Deconvolution (MCKD) method is a deconvolution method for extracting bearing faults from vibration data. It uses Correlation Kurtosis (CK) as the objective function and solves the optimal filter through iterative calculation. This method can enhance the periodic pulses in the fault information and has a good effect in fault detection.

[0003] Existing blind deconvolution methods usually have difficulty in determining whether the result is a global optimal solution. When processing strong noise signals or weak fault signals, different initial filter coefficients will obtain different filtering results. In MCKD, although the initial filter coefficients of MCKD can be set manually, unlike the first-order shifted MCKD, the high-order shifted MCKD is still likely to converge to a local optimal solution. The reason is that the partial derivative function of the relevant kurtosis of the objective function to the filter coefficient is not strictly monotonically convergent, so a local optimal solution is inevitable. Summary of the invention

[0004] One or more embodiments of this specification provide a bearing fault diagnosis method for optimizing filtered MCKD, including:

[0005] S1. Collect vibration signals and perform Fourier transform on the spectrum of the vibration signals to obtain key functions;

[0006] S2. Set the initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands of different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all the results;

[0007] S3. Design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD;

[0008] S4. Calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum value of HSK as the global optimal solution of MCKD, and complete the fault diagnosis.

[0009] Furthermore, the specific method of collecting the vibration signal and performing Fourier transform on the spectrum of the vibration signal to obtain the key function is:

[0010] The vibration signal is subjected to Fourier transform to obtain the spectrum, and the spectrum is subjected to Fourier transform to obtain the key function.

[0011] Furthermore, the specific method of performing Fourier transform on the vibration signal to obtain the spectrum is:

[0012] The collected vibration signal is x(t), and the spectrum is obtained by Fourier transform using formula 1:

[0013]

[0014] The absolute value |X(f)| of the spectrum X(f) is a discrete sequence containing only the real part.

[0015] Furthermore, the specific method of performing Fourier transform on the spectrum to obtain the key function is:

[0016] Assume v(n) = X(f), n = 1, 2, ..., L, L is the sequence length, v(n) is a discrete non-negative sequence, and the Fourier transform function of v(n) is calculated as shown in Formula 2:

[0017]

[0018] Perform Fourier transform on the amplitude sequence of the spectrum, and the calculated V(u) contains real and imaginary parts. V(u) is the key function of the signal x(t).

[0019] Furthermore, the initial intercept length is set, and the key function is subjected to inverse Fourier transform based on the initial intercept length to obtain a spectrum trend, and the filter bands of different bandwidths and center frequencies are divided by using the minimum value of the trend spectrum as the boundary of the spectrum segmentation. The specific method is:

[0020] Perform inverse Fourier transform on the first part of the key function V(u), set the initial intercept length k, and use V k (u) represents the new sequence obtained by taking the first k numbers of the key function V(u) and setting the subsequent numbers to zero. The spectrum trend is shown in Formula 3:

[0021]

[0022] A trend spectrum is obtained according to the frequency spectrum trend, and filtering bands of different bandwidths and center frequencies are divided according to the minimum value of the trend spectrum.

[0023] Furthermore, step S2 further includes: increasing the interception length, repeatedly dividing the spectrum, recording all results, and constructing a multi-layer tower-like spectrum segmentation framework.

[0024] Furthermore, the method further comprises:

[0025] After selecting the filtering result corresponding to the HSK maximum value, spectrum analysis and envelope demodulation are performed on the selected filtering result to complete the fault diagnosis.

[0026] One or more embodiments of this specification provide a bearing fault diagnosis system for optimizing filtering MCKD, including:

[0027] Signal processing module: used to collect vibration signals and perform Fourier transform on the spectrum of vibration signals to obtain key functions;

[0028] Spectrum segmentation module: used to set the initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands with different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all the results;

[0029] Filter construction module: used to design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD;

[0030] Fault diagnosis module: It is used to calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum value of HSK as the global optimal solution of MCKD, and complete fault diagnosis.

[0031] One or more embodiments of the present specification provide an electronic device, including:

[0032] processor; and,

[0033] A memory arranged to store computer executable instructions, which, when executed, cause the processor to implement the steps of the above-mentioned bearing fault diagnosis method for optimizing filtered MCKD.

[0034] One or more embodiments of the present specification provide a storage medium for storing computer executable instructions, which, when executed, implement the steps of the above-mentioned bearing fault diagnosis method of optimizing filtered MCKD.

[0035] By adopting the embodiment of the present invention, the spectrum segmentation boundary is determined and the filter band is divided through Fourier transform and inverse transform, so that different frequency components can be accurately located and characteristic information in the vibration signal can be effectively extracted; the initial filter coefficient is designed by using Mayer wavelet filter and used for MCKD, which can enhance the processing capability of fault signals; the fault characteristics are quantified by harmonic spectrum kurtosis HSK and the global optimal solution is selected, which can efficiently and accurately diagnose faults, improve the reliability and accuracy of fault diagnosis, help quickly locate the root cause of equipment failure, reduce equipment maintenance costs and downtime, and improve equipment operation stability and safety.

[0036] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 A flow chart of a bearing fault diagnosis method for optimizing filtered MCKD provided in one or more embodiments of this specification;

[0039] Figure 2 It is a schematic diagram of a bearing outer ring fault test bench and a faulty part in a specific embodiment of this specification;

[0040] Figure 3 Schematic diagram of the waveform, spectrum and envelope spectrum of the bearing outer ring fault signal in the specific embodiment of this specification;

[0041] Figure 4 A schematic diagram of the harmonic kurtosis diagram of the bearing outer ring fault signal processing and the spectrum and envelope spectrum of the processing result in a specific embodiment of this specification;

[0042] Figure 5 This is a schematic diagram of the frequency spectrum and envelope spectrum of the bearing outer ring fault signal processed by bandpass filtering in a specific embodiment of this specification;

[0043] Figure 6 This is a schematic diagram of the frequency spectrum and envelope spectrum of the bearing outer ring fault signal processed by the MCKD method without special processing in the specific embodiment of this specification;

[0044] Figure 7 This is a schematic diagram of a bearing inner ring fault test bench in a specific embodiment of this specification;

[0045] Figure 8 A schematic diagram of the waveform, spectrum and envelope spectrum of a bearing inner race fault signal in a specific embodiment of this specification;

[0046] Fig. 9 A schematic diagram of the harmonic kurtosis diagram of the bearing inner race fault signal processing and the frequency spectrum and envelope spectrum of the processing result in a specific embodiment of this specification;

[0047] Fig.10 This is a schematic diagram of the frequency spectrum and envelope spectrum of a bearing inner race fault signal processed by bandpass filtering in a specific embodiment of this specification;

[0048] Fig.11 This is a schematic diagram of the frequency spectrum and envelope spectrum of the bearing inner ring fault signal processed by the MCKD method without special processing in the specific embodiment of this specification;

[0049] Fig.12 A schematic diagram of the composition of a bearing fault diagnosis system for optimizing filtering MCKD provided in one or more embodiments of this specification;

[0050] Fig.13 A schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0052] Method Embodiment

[0053] According to an embodiment of the present invention, a bearing fault diagnosis method for optimizing filtered MCKD is provided. Figure 1 A flowchart of a bearing fault diagnosis method for optimizing filtering MCKD provided in one or more embodiments of this specification, such as Figure 1 As shown, the bearing fault diagnosis method of optimizing the filtering MCKD according to an embodiment of the present invention specifically includes:

[0054] S1. Collect vibration signals and perform Fourier transform on the frequency spectrum of the vibration signals to obtain key functions.

[0055] First, the vibration signal is Fourier transformed to obtain the spectrum, and the spectrum is Fourier transformed to obtain the key function.

[0056] The specific method of Fourier transforming the vibration signal to obtain the spectrum is:

[0057] The collected vibration signal is x(t), and the spectrum is obtained by Fourier transform using formula 1:

[0058]

[0059] The absolute value |X(f)| of the spectrum X(f) is a discrete sequence containing only the real part.

[0060] The specific method of Fourier transforming the spectrum to obtain the key function is:

[0061] Assume v(n) = X(f), n = 1, 2, ..., L, L is the sequence length, v(n) is a discrete non-negative sequence, and the Fourier transform function of v(n) is calculated as shown in Formula 2:

[0062]

[0063] Perform Fourier transform on the amplitude sequence of the spectrum, and the calculated V(u) contains real and imaginary parts. V(u) is the key function of the signal x(t).

[0064] S2. Set the initial truncation length, perform inverse Fourier transform on the key function based on the initial truncation length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands with different bandwidths and center frequencies, increase the truncation length, repeat the above steps, and record all the results.

[0065] Perform inverse Fourier transform on the first part of the key function V(u). In Fourier transform, performing inverse Fourier transform on the low-frequency series in the spectrum can reflect the trend of the original waveform, so performing inverse Fourier transform on the low-frequency series of the "key function" can reflect the trend of the vibration signal spectrum.

[0066] Set the initial intercept length k, use V k (u) represents the new sequence obtained by taking the first k numbers of the key function V(u) and setting the subsequent numbers to zero. The spectrum trend is shown in Formula 3:

[0067]

[0068] A trend spectrum is obtained according to the frequency spectrum trend, and filtering bands of different bandwidths and center frequencies are divided according to the minimum value of the trend spectrum.

[0069] Increase the intercept length, repeatedly divide the spectrum, record all the results, build a multi-layer tower-like spectrum segmentation framework, and construct the obtained filtering results into a multi-layer tower-like frequency band distribution diagram. Continuously increase the intercept length k, and different spectrum trends will be obtained. When the k value is small, the spectrum trend is smooth and has a small number of minimum values; when the k value increases, the spectrum trend gradually becomes complex and the number of minimum points also increases.

[0070] S3. Design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD.

[0071] The spectrum has been divided into filter band components with different center frequencies and bandwidths. Each band component contains fault characteristics or interference components to varying degrees. A Mayer wavelet filter is designed based on the divided band areas, and these filter coefficients are used as the initial filter coefficients of MCKD to improve its effectiveness and robustness.

[0072] S4. Calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum value of HSK as the global optimal solution of MCKD, and complete the fault diagnosis.

[0073] Specifically, the harmonic spectrum kurtosis HSK is used to quantify the fault characteristics of the MCKD filtering results under different initial filters, and the filtering result with the largest HSK value is selected as the global optimal solution of MCKD. Finally, spectrum analysis and envelope demodulation are performed to complete the fault diagnosis.

[0074] The present invention takes the bearing outer ring fault signal on the rotor gear comprehensive fault simulation test bench as a specific embodiment to further illustrate the above method:

[0075] The bearing model used is 6205 deep groove ball bearing, which has an outer ring fault. The motor speed is 3000rpm, the sampling frequency is 16384Hz, and the number of sampling points is 16384. The calculation shows that the characteristic frequency of the bearing outer ring fault is f0=179.5Hz. The experimental platform and the bearing outer ring fault part are as follows Figure 2 shown.

[0076] The waveform, spectrum and envelope spectrum of the bearing outer ring fault signal are as follows: Figure 3 As shown in the figure, due to the influence of strong noise, the periodic pulses in the time domain waveform are completely submerged. Although there are prominent frequency bands in the spectrum, it is difficult to observe any effective information from them due to noise interference. There is also no obvious fault information in the envelope spectrum, making it difficult to determine whether there is a fault.

[0077] The method proposed in the present invention is used to process the bearing outer ring fault signal. The processing result is as follows: Figure 4 As shown in the harmonic kurtosis diagram, it can be seen that the optimal filter signal corresponding to the maximum HSK value is located in the third layer, with a bandwidth of 1253Hz and a center frequency f c =1567Hz, and the center frequency is close to the prominent frequency band in the original signal spectrum. The spectrum analysis and envelope demodulation of the filtered signal show that the outer race fault characteristic frequency and the obvious fifth harmonic appear in its envelope spectrum, indicating that the signal comes from a bearing with an outer race fault.

[0078] The present invention uses the MCKD method without special processing of the bandpass filter and the initial filter to process the bearing outer ring fault signal. The result of the bandpass filter is as follows: Figure 5 As shown, this frequency band has the same boundary as the optimal initial filter in the method of the present invention. It can be observed that this frequency band contains the most prominent frequency band in the signal spectrum, and the outer ring fault characteristic frequency appears in the envelope spectrum. However, due to the short frequency band and the influence of strong noise, effective fault diagnosis cannot be performed.

[0079] The signal is processed using the MCKD method without any special processing, and the results are as follows Figure 6 As shown in Figure 2, the entire spectrum of the MCKD filtered signal is enhanced, making it difficult to determine whether there is a fault resonance band, and the envelope spectrum does not contain obvious fault information.

[0080] The comparison shows that the method of the present invention has good adaptability and can also detect the maximum fault resonance frequency band.

[0081] For the bearing inner ring fault signal, in this embodiment, the bearing model is ER-12K, there is an artificial inner ring fault, the click speed is 1451.7rpm, the sampling frequency is 12000Hz, the number of sampling points is 12000, and the calculation shows that the bearing inner ring fault characteristic frequency is f0=119.74Hz. Figure 7 shown.

[0082] The waveform, spectrum and envelope spectrum of the bearing outer ring fault signal are as follows: Figure 8 As shown, it can be observed that there is no obvious periodic pulse characteristic in the time domain waveform, and the theoretical inner race fault characteristic frequency is observed in the envelope spectrum, but there are multiple meaningless high-amplitude peaks nearby, which makes it difficult to perform accurate fault diagnosis.

[0083] The method proposed in the present invention is used to process the bearing inner ring fault signal. The processing result is as follows: Fig. 9 The optimal filter signal corresponding to the maximum HSK value is located at the 10th layer, with a bandwidth of 568Hz and a center frequency of f c=3174Hz, this frequency band is completely submerged by noise in the original signal spectrum, which fully meets the experimental conditions under weak faults. The spectrum analysis and envelope demodulation of the filtered signal show that the resonance band of the inner ring fault of the bearing is enhanced, and there is an obvious characteristic frequency of the inner ring fault and its quadruple harmonic in the envelope spectrum, which meets the diagnostic basis of the inner ring bearing fault.

[0084] In order to further illustrate the superiority of the method proposed in the present invention, the bearing inner race fault signal is processed using the MCKD method without special processing of the bandpass filter and the initial filter.

[0085] The result of bandpass filtering is as follows Fig.10 As shown, this frequency band has the same boundary as the optimal initial filter in the method of the present invention. It can be observed that the inner race fault characteristic frequency appears in the envelope spectrum of its corresponding filter component, but due to the influence of the surrounding large peaks, it is difficult to perform effective fault diagnosis.

[0086] The signal is processed using the MCKD method without any special processing, and the results are as follows Fig.11 Obviously, if the initial filter of MCKD is not specially processed, it is difficult to obtain the ideal result, which shows that it is difficult for the original MCKD to extract weak fault information.

[0087] The comparison shows that the method of the present invention has good adaptability.

[0088] The beneficial effects of the present invention are as follows:

[0089] By adopting the embodiment of the present invention, the spectrum segmentation boundary is determined and the filter band is divided through Fourier transform and inverse transform, so that different frequency components can be accurately located and characteristic information in the vibration signal can be effectively extracted; the initial filter coefficient is designed by using Mayer wavelet filter and used for MCKD, which can enhance the processing capability of fault signals; the fault characteristics are quantified by harmonic spectrum kurtosis HSK and the global optimal solution is selected, which can efficiently and accurately diagnose faults, improve the reliability and accuracy of fault diagnosis, help quickly locate the root cause of equipment failure, reduce equipment maintenance costs and downtime, and improve equipment operation stability and safety.

[0090] System Example

[0091] According to an embodiment of the present invention, a bearing fault diagnosis system for optimizing filtered MCKD is provided. Fig.12 A schematic diagram of the composition of a bearing fault diagnosis system for optimizing filtering MCKD provided in one or more embodiments of this specification, such as Fig.12 As shown, the bearing fault diagnosis system of the optimized filtered MCKD according to the embodiment of the present invention specifically includes:

[0092] Signal processing module 120: used to collect vibration signals and perform Fourier transform on the frequency spectrum of the vibration signals to obtain key functions;

[0093] Spectrum segmentation module 122: used to set an initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain a spectrum trend, use the minimum value of the trend spectrum as the boundary of spectrum segmentation to divide the filter bands of different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all results;

[0094] A filter construction module 124 is used to design a Mayer wavelet filter based on the obtained filtering frequency band, and use the designed Mayer wavelet filter coefficients as initial filter coefficients of MCKD;

[0095] Fault diagnosis module 126: used to calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum HSK value as the global optimal solution of MCKD, and complete fault diagnosis.

[0096] The embodiment of the present invention is a system embodiment corresponding to the above-mentioned method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0097] Device Example 1

[0098] An embodiment of the present invention provides an electronic device, such as Fig.13 As shown, it includes: a memory 130, a processor 132, and a computer program stored in the memory 130 and executable on the processor 132, and when the computer program is executed by the processor 132, the following method steps are implemented:

[0099] S1. Collect vibration signals and perform Fourier transform on the spectrum of the vibration signals to obtain key functions;

[0100] S2. Set the initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands of different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all the results;

[0101] S3. Design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD;

[0102] S4. Calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum value of HSK as the global optimal solution of MCKD, and complete the fault diagnosis.

[0103] Device Example 2

[0104] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 132, the following method steps are implemented:

[0105] S1. Collect vibration signals and perform Fourier transform on the spectrum of the vibration signals to obtain key functions;

[0106] S2. Set the initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands of different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all the results;

[0107] S3. Design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD;

[0108] S4. Calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum value of HSK as the global optimal solution of MCKD, and complete the fault diagnosis.

[0109] The computer-readable storage medium in this embodiment includes, but is not limited to, ROM, RAM, magnetic disk or optical disk, etc.

[0110] 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 present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bearing fault diagnosis method for optimizing filtered MCKD, characterized in that: include: S1. Collect vibration signals and perform Fourier transform on the spectrum of the vibration signals to obtain key functions; S2. Set the initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands of different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all the results; S3. Design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD; S4. Calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with the harmonic spectrum kurtosis HSK, select the filtering result corresponding to the maximum HSK value as the global optimal solution of MCKD, and complete the fault diagnosis.

2. The method according to claim 1, characterized in that: The specific method of collecting the vibration signal and performing Fourier transform on the spectrum of the vibration signal to obtain the key function is: The vibration signal is subjected to Fourier transform to obtain the spectrum, and the spectrum is subjected to Fourier transform to obtain the key function.

3. The method according to claim 2, characterized in that The specific method of performing Fourier transform on the vibration signal to obtain the spectrum is: The collected vibration signal is x(t), and the spectrum is obtained by Fourier transform using formula 1: The absolute value |X(f)| of the spectrum X(f) is a discrete sequence containing only the real part.

4. The method according to claim 3, characterized in that The specific method of performing Fourier transform on the spectrum to obtain the key function is: Assume v(n) = X(f), n = 1, 2, ..., L, L is the sequence length, v(n) is a discrete non-negative sequence, and the Fourier transform function of v(n) is calculated as shown in Formula 2: Perform Fourier transform on the amplitude sequence of the spectrum, and the calculated V(u) contains real and imaginary parts. V(u) is the key function of the signal x(t).

5. The method according to claim 4, characterized in that The specific method of setting the initial intercept length, performing inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, and using the minimum value of the trend spectrum as the boundary of spectrum segmentation to divide the filter bands of different bandwidths and center frequencies is as follows: Perform inverse Fourier transform on the first part of the key function V(u), set the initial intercept length k, and use V k (u) represents the new sequence obtained by taking the first k numbers of the key function V(u) and setting the subsequent numbers to zero. The spectrum trend is shown in Formula 3: A trend spectrum is obtained according to the frequency spectrum trend, and filtering bands of different bandwidths and center frequencies are divided according to the minimum value of the trend spectrum.

6. The method according to claim 1, characterized in that Step S2 further includes: increasing the interception length, repeatedly dividing the spectrum, recording all results, and constructing a multi-layer tower-like spectrum segmentation framework.

7. The method according to claim 1, characterized in that The method further comprises: After selecting the filtering result corresponding to the HSK maximum value, spectrum analysis and envelope demodulation are performed on the selected filtering result to complete the fault diagnosis.

8. A bearing fault diagnosis system with optimized filtering MCKD, characterized in that: include: Signal processing module: used to collect vibration signals and perform Fourier transform on the spectrum of vibration signals to obtain key functions; Spectrum segmentation module: used to set the initial intercept length, perform inverse Fourier transform on the key function based on the initial intercept length to obtain the spectrum trend, use the minimum value of the trend spectrum as the boundary of the spectrum segmentation to divide the filter bands with different bandwidths and center frequencies, increase the intercept length, repeat the above steps, and record all the results; Filter construction module: used to design a Mayer wavelet filter based on the obtained filter band, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of MCKD; Fault diagnosis module: It is used to calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals with harmonic spectral kurtosis (HSK), select the filtering result corresponding to the maximum value of HSK as the global optimal solution of MCKD, and complete fault diagnosis.

9. An electronic device, characterized in that: include: processor; as well as, A memory arranged to store computer executable instructions, which, when executed, cause the processor to implement the steps of the bearing fault diagnosis method for optimizing filtered MCKD according to any one of claims 1 to 7.

10. A storage medium, characterized in that: Used to store computer executable instructions, which, when executed, implement the steps of the bearing fault diagnosis method of optimizing filtered MCKD as described in any one of claims 1 to 7.

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