A bearing fault diagnosis method and system of optimized filtering MCKD

Abstract

The embodiment of the specification provides a bearing fault diagnosis method and system of optimized filtering MCKD, wherein the method comprises the following steps: collecting a vibration signal, performing Fourier transform on a frequency spectrum of the vibration signal to obtain a key function; setting an initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a frequency spectrum trend, taking a minimum value of the trend spectrum as a boundary of spectrum segmentation to divide a filtering frequency band with different bandwidths and center frequencies, increasing the truncation length, repeating the above steps, and recording all results; designing a Mayer wave filter based on the obtained filtering frequency band, and taking the designed Mayer wave filter coefficient as an initial filter coefficient of the MCKD; calculating MCKD filtering results of different initial filter coefficients, quantifying fault characteristics of all filtering signals by a harmonic spectrum kurtosis (HSK), selecting a filtering result corresponding to a maximum value of the HSK as a global optimal solution of the MCKD, and completing fault diagnosis.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of fault diagnosis, in particular to a bearing fault diagnosis method and system based on optimized filtering MCKD. BACKGROUND

[0002] Studies have shown that the collected signals are a mixture of multiple convolutions of fault excitation sources, noise and transmission paths, and therefore some scholars have proposed the concept of blind deconvolution, hoping to design an inverse filter to make the filtered signal similar to the fault source signal. In order to better measure fault information, the existing maximum correlation kurtosis deconvolution (MCKD) method is a deconvolution method for extracting bearing faults from vibration data, which takes 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 good effect in fault detection.

[0003] The existing blind deconvolution method is usually difficult to determine whether the result is a global optimal solution, and different initial filter coefficients will obtain different filtering results when processing strong noise signals or weak fault signals. In MCKD, although the initial filter coefficients of MCKD can be set artificially, unlike the first-order shift MCKD, the high-order shift MCKD still has the risk of converging to a local optimal solution, because the partial derivative of the correlation kurtosis as the objective function with respect to the filter coefficients is not strictly monotonic convergence, so the local optimal solution is inevitable. SUMMARY

[0004] One or more embodiments of the present specification provide a bearing fault diagnosis method based on optimized filtering MCKD, comprising:

[0005] S1. Collecting a vibration signal, performing Fourier transform on the frequency spectrum of the vibration signal to obtain a key function;

[0006] S2. Setting an initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a spectral trend, dividing different bandwidths and center frequencies of the filter band by taking the minimum value of the trend spectrum as the boundary of spectral segmentation, increasing the truncation length, repeating the above steps, and recording all results;

[0007] S3. Designing a Mayer wavelet filter based on the obtained filter band, and taking the designed Mayer wavelet filter coefficient as the initial filter coefficient of MCKD;

[0008] S4. Calculate the MCKD filtering results of different initial filter coefficients, quantify the fault characteristics of all filtered signals by 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.

[0009] Further, the vibration signal is collected, and a key function is obtained by performing Fourier transform on a frequency spectrum of the vibration signal.

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

[0011] Further, the Fourier transform of the vibration signal to obtain the frequency spectrum is specifically as follows:

[0012] The collected vibration signal is , and the Fourier transform of the vibration signal is performed by formula 1 to obtain a frequency spectrum:

[0013] Formula 1;

[0014] The absolute value of the frequency spectrum is a discrete sequence containing only a real part.

[0015] Further, the Fourier transform of the frequency spectrum to obtain the key function is specifically as follows:

[0016] Let , L is the length of the sequence, is a discrete non-negative sequence, and the Fourier transform function of is shown in formula 2:

[0017] Formula 2;

[0018] The Fourier transform of the amplitude sequence of the frequency spectrum is performed, and the calculated contains a real part and an imaginary part, is a key function of the signal .

[0019] Further, the Fourier inverse transform of the key function is performed based on the initial truncation length to obtain a frequency spectrum trend, and the filtering frequency band of different bandwidths and center frequencies divided by the minimum value of the trend spectrum as the boundary of the frequency spectrum segmentation is specifically as follows:

[0020] The Fourier inverse transform of a part of the sequence at the beginning of the key function is performed, the initial truncation length is set, and is used to represent the first of the key function ​The new sequence is obtained by setting the number of the new sequence to zero and setting the subsequent values to zero, and the spectral trend is shown in formula 3:

[0021] Formula 3

[0022] According to the spectral trend, a trend spectrum is obtained, and a filter band with different bandwidths and center frequencies is divided by the minimum value of the trend spectrum.

[0023] Further, step S2 further comprises: increasing the intercept length, repeating the division of the spectrum, recording all results, and constructing a multi-layer tower type spectrum segmentation framework.

[0024] Further, the method further comprises:

[0025] After selecting the filter result corresponding to the maximum HSK, the selected filter result is subjected to spectral analysis and envelope demodulation, and fault diagnosis is completed.

[0026] One or more embodiments of the present specification provide a bearing fault diagnosis system for optimizing filter MCKD, comprising:

[0027] The signal processing module is used for collecting the vibration signal and performing Fourier transform on the spectrum of the vibration signal to obtain a key function;

[0028] The spectrum segmentation module is used for setting an initial intercept length, performing inverse Fourier transform on the key function based on the initial intercept length to obtain a spectral trend, dividing a filter band with different bandwidths and center frequencies by the minimum value of the trend spectrum as the boundary of the spectrum segmentation, increasing the intercept length, repeating the above steps, and recording all results;

[0029] The filter construction module is used for designing a Mayer wavelet filter based on the obtained filter band, and using the designed Mayer wavelet filter coefficient as the initial filter coefficient of the MCKD;

[0030] The fault diagnosis module is used for calculating the MCKD filter result of different initial filter coefficients, quantifying the fault characteristics of all filter signals by harmonic spectrum kurtosis (HSK), selecting the filter result corresponding to the maximum HSK as the global optimal solution of the MCKD, and completing fault diagnosis.

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

[0032] A processor; and

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

[0034] The one or more embodiments of the 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 optimized filtering MCKD.

[0035] By adopting the embodiments of the present application, the frequency spectrum segmentation boundary is determined by Fourier transform and inverse transform, and the filtering frequency band is divided, so that different frequency components can be accurately positioned, and the characteristic information in the vibration signal can be effectively extracted; the initial filter coefficients are designed by using the Mayer wavelet filter, and are used for MCKD, so that the processing capability for fault signals can be enhanced; the fault characteristics are quantified by using the harmonic spectrum kurtosis HSK, and the global optimal solution is selected, so that the fault can be efficiently and accurately diagnosed, the reliability and precision of the fault diagnosis are improved, the equipment fault root cause can be quickly located, the equipment maintenance cost and downtime are reduced, and the equipment operation stability and safety are improved.

[0036] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the one or more embodiments of the specification or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without creating labor intensity.

[0038] Figure 1 A flowchart of a bearing fault diagnosis method of optimized filtering MCKD is provided for the one or more embodiments of the specification.

[0039] Figure 2 A bearing outer ring fault test bench and a fault part schematic diagram are provided for the specific embodiments of the specification.

[0040] Figure 3 A waveform, frequency spectrum and envelope spectrum schematic diagram of the bearing outer ring fault signal is provided for the specific embodiments of the specification.

[0041] Figure 4 A harmonic kurtosis diagram of the bearing outer ring fault signal processing and a frequency spectrum and envelope spectrum schematic diagram of the processing result are provided for the specific embodiments of the specification.

[0042] Figure 5 A frequency spectrum and envelope spectrum schematic diagram of the bearing outer ring fault signal processed by using a band-pass filter is provided for the specific embodiments of the specification.

[0043] Figure 6 Fig. 11 is a schematic diagram of a spectrum and an envelope spectrum of a bearing outer ring fault signal processed by the MCKD method without special processing used in the embodiments of the present specification;

[0044] Figure 7 Fig. 12 is a schematic diagram of a bearing inner ring fault test bed used in the embodiments of the present specification;

[0045] Figure 8 Fig. 13 is a schematic diagram of a waveform, a spectrum and an envelope spectrum of a bearing inner ring fault signal used in the embodiments of the present specification;

[0046] Figure 9 Fig. 14 is a schematic diagram of a harmonic kurtogram and a spectrum and an envelope spectrum of a processing result of a bearing inner ring fault signal used in the embodiments of the present specification;

[0047] Figure 10 Fig. 15 is a schematic diagram of a spectrum and an envelope spectrum of a bearing inner ring fault signal processed by a band-pass filter used in the embodiments of the present specification;

[0048] Figure 11 Fig. 16 is a schematic diagram of a spectrum and an envelope spectrum of a bearing inner ring fault signal processed by the MCKD method without special processing used in the embodiments of the present specification;

[0049] Figure 12 Fig. 17 is a schematic diagram of a bearing fault diagnosis system using the optimized filtering MCKD provided by one or more embodiments of the present specification;

[0050] Figure 13 Fig. 18 is a schematic diagram of an electronic device provided by one or more embodiments of the present specification. DETAILED DESCRIPTION

[0051] In order to enable persons skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be described clearly and completely below with reference to the drawings in one or more embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present specification, rather than all the embodiments. Based on one or more embodiments of the present specification, all other embodiments obtained by persons skilled in the art without creative labor should belong to the protection scope of the present document.

[0052] Method embodiments

[0053] According to the embodiments of the present application, a bearing fault diagnosis method using the optimized filtering MCKD is provided, Figure 1 Fig. 17 is a schematic diagram of a bearing fault diagnosis system using the optimized filtering MCKD provided by one or more embodiments of the present specification; Figure 1As shown, the bearing fault diagnosis method of the optimized filtering MCKD according to the embodiment of the application specifically comprises:

[0054] S1. Collecting a vibration signal, and performing Fourier transform on a frequency spectrum of the vibration signal to obtain a key function.

[0055] First, performing Fourier transform on the vibration signal to obtain a frequency spectrum, and performing Fourier transform on the frequency spectrum to obtain the key function.

[0056] The specific method of performing Fourier transform on the vibration signal to obtain the frequency spectrum is as follows:

[0057] The collected vibration signal is , and the Fourier transform is performed on the vibration signal by formula 1 to obtain the frequency spectrum:

[0058] Formula 1;

[0059] The absolute value of the frequency spectrum is a discrete sequence containing only real parts.

[0060] The specific method of performing Fourier transform on the frequency spectrum to obtain the key function is as follows:

[0061] Let , L be the sequence length, be a discrete non-negative sequence, and the Fourier transform function of be calculated as shown in formula 2:

[0062] Formula 2;

[0063] The Fourier transform is performed on the amplitude sequence of the frequency spectrum, and the calculated contains real and imaginary parts, is the key function of the signal .

[0064] S2. Setting an initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a frequency spectrum trend, dividing different bandwidths and center frequencies of the filter band by taking the minimum value of the trend spectrum as the boundary of the frequency spectrum segmentation, increasing the truncation length, repeating the above steps, and recording all results.

[0065] The inverse Fourier transform is performed on a part of the sequence at the beginning of the key function , and the inverse Fourier transform is performed on the low-frequency sequence in the frequency spectrum in the Fourier transform, which can reflect the trend of the original waveform, so the inverse Fourier transform is performed on the low-frequency sequence of the key function, which can reflect the trend of the vibration signal spectrum.

[0066] The initial truncation length is set, ​The key function is represented The first The new sequence obtained by setting the first number of the key function to zero and setting the subsequent numbers to zero, and the spectral trend is shown in formula 3:

[0067] Formula 3;

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

[0069] Increase the intercept length, repeat the division of the spectrum, record all the results, construct a multi-layer tower type spectrum segmentation framework, and construct a multi-layer tower type frequency band distribution diagram with the obtained filtering results. Continuously increase the intercept length , different spectral trends are obtained, when The spectral trend is smooth and has a small number of minimum values; when The spectral trend gradually becomes complex, and the number of minimum points also increases.

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

[0071] The spectrum has been divided into different center frequency and bandwidth filtering frequency band components, and each frequency band component contains different degrees of fault characteristics or interference components. According to the designed Mayer wavelet filter of the divided frequency band region, the filter coefficients are used as the initial filter coefficients of the 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 spectrum kurtosis (HSK), select the filtering result corresponding to the maximum HSK value as the global optimal solution of the MCKD, and complete the fault diagnosis.

[0073] Specifically, the fault characteristics of the MCKD filtering results under different initial filters are quantified by harmonic spectrum kurtosis HSK, the filtering result with the maximum HSK value is selected as the global optimal solution of the MCKD, and finally the spectrum analysis and envelope demodulation are completed to complete the fault diagnosis.

[0074] The present application takes the bearing outer ring fault signal on the rotor gear comprehensive fault simulation experiment table as a specific embodiment, and further illustrates the above method:

[0075] The bearing used is a 6205 deep groove ball bearing, which has a machining outer ring fault, the motor speed is 3000 rpm, the sampling frequency is 16384 Hz, the sampling point number is 16384, and the bearing outer ring fault characteristic frequency is calculated to be Hz. The experimental platform and the fault part of the bearing outer ring are shown in Figure 2

[0076] The waveform, spectrum and envelope spectrum of the bearing outer ring fault signal are shown in Figure 3 Due to the influence of strong noise, the periodic pulse in the time domain waveform is completely submerged, although there is a prominent frequency band in the spectrum, it is difficult to observe any effective information from the noise interference, and there is no obvious fault information in the envelope spectrum, so it is difficult to determine whether there is a fault.

[0077] The bearing outer ring fault signal is processed using the method proposed in the application, and the processing result is shown in Figure 4 The optimal filter signal corresponding to the maximum HSK value is located in the third layer, and the bandwidth is 1253 Hz, and the center frequency is Hz, and the center frequency is close to the prominent frequency band in the original signal spectrum. The spectrum analysis and envelope demodulation of the filter signal show that the envelope spectrum appears the outer ring fault characteristic frequency and obvious fifth harmonic, which indicates that the signal comes from an outer ring fault bearing.

[0078] The bearing outer ring fault signal is processed using the band-pass filter and the MCKD method without special processing of the initial filter, and the result of the band-pass filter is shown in Figure 5 The frequency band has the same boundary as the optimal initial filter in the method of the application, and it can be observed that the frequency band contains the most prominent frequency band in the signal spectrum, and the envelope spectrum appears the outer ring fault characteristic frequency, but 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 special processing, and the result is shown in Figure 6 The entire spectrum of the MCKD filter signal is enhanced, it is difficult to determine whether there is a fault resonance band, and the envelope spectrum also has no obvious fault information.

[0080] It is concluded that the method of the application has good adaptability and can detect the maximum fault resonance frequency band.

[0081] For the bearing inner ring fault signal, in this embodiment, the bearing type is ER-12K, and the inner ring fault is artificially processed, the click rotation speed is 1451.7 rpm, the sampling frequency is 12000 Hz, the sampling point number is 12000, and the bearing inner ring fault characteristic frequency is calculated as Hz. The experimental platform is shown in Figure 7

[0082] The waveform, spectrum and envelope spectrum of the bearing outer ring fault signal are shown in Figure 8 ​​As shown in the figure, no obvious periodic pulse characteristics can be observed in the time domain waveform, and the theoretical inner ring fault characteristic frequency is observed in the envelope spectrum, but there are multiple meaningless high amplitude peaks near it, which makes it difficult to accurately diagnose the fault.

[0083] The bearing inner ring fault signal is processed using the method proposed in the application, and the processing result is as shown in the figure Figure 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 2.5kHz. Hz, and this frequency band is completely submerged in noise in the original signal spectrum, completely meeting the experimental conditions under weak fault. Through frequency spectrum analysis and envelope demodulation of the filter signal, it can be observed that the resonance band of the bearing inner ring fault is enhanced, and there are obvious inner ring fault characteristic frequency and its fourth 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 application, the bearing inner ring fault signal is processed using the MCKD method with a band-pass filter and an initial filter without special processing.

[0085] The result of the band-pass filter is as shown in the figure Figure 10 . The frequency band has the same boundary as the optimal initial filter in the method of the application, and it can be observed that the inner ring fault characteristic frequency appears in the envelope spectrum of the corresponding filter component, but due to the influence of the surrounding large peak, it is difficult to effectively diagnose the fault.

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

[0087] It is concluded that the method of the application has good adaptability.

[0088] The application has the following beneficial effects:

[0089] By using the embodiments of the application, the frequency spectrum segmentation boundary and the filter frequency band are determined through Fourier transform and inverse transform, different frequency components can be accurately positioned, and the characteristic information in the vibration signal can be effectively extracted; the initial filter coefficient is designed using the Mayer wave filter and used for MCKD, which can enhance the processing capacity of the fault signal; the fault characteristics are quantified by the harmonic spectrum kurtosis HSK, and the global optimal solution is selected, which can efficiently and accurately diagnose the fault, improve the reliability and accuracy of fault diagnosis, help to quickly locate the equipment fault source, reduce the equipment maintenance cost and downtime, and improve the equipment operation stability and safety.

[0090] System embodiment

[0091] According to the embodiment of the present application, a bearing fault diagnosis system based on optimized filtering MCKD is provided, Figure 12 A schematic diagram of a bearing fault diagnosis system based on optimized filtering MCKD is provided for one or more embodiments of the present application, as shown in the figure, Figure 12 The bearing fault diagnosis system based on optimized filtering MCKD according to the embodiment of the present application specifically comprises:

[0092] The signal processing module 120 is configured to collect a vibration signal and perform Fourier transform on the frequency spectrum of the vibration signal to obtain a key function;

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

[0094] The filter construction module 124 is configured to design a Mayer wavelet filter based on the obtained filter bands, and use the designed Mayer wavelet filter coefficients as the initial filter coefficients of the MCKD;

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

[0096] The embodiment of the present application is a system embodiment corresponding to the above-mentioned method embodiment, and the specific operation of each module can be understood with reference to the description of the method embodiment, which will not be repeated here.

[0097] Device embodiment one

[0098] The embodiment of the present application provides an electronic device, as shown in the figure, comprising a memory 130, a processor 132, and a computer program stored on the memory 130 and executable on the processor 132, wherein the computer program is executed by the processor 132 to implement the following method steps: Figure 13

[0099] S1. Collect a vibration signal and perform Fourier transform on the frequency spectrum of the vibration signal to obtain a key function;

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

[0101] S3. Design a Mayer wave filter based on the obtained filter band, and take the designed Mayer wave filter coefficient as the initial filter coefficient of the MCKD.

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

[0103] Device embodiment two

[0104] The embodiment of the application provides a computer readable storage medium, and information transmission is stored on the computer readable storage medium. The program is executed by the processor 132 to realize the following method steps:

[0105] S1. Collect a vibration signal, and perform Fourier transform on the frequency spectrum of the vibration signal to obtain a key function;

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

[0107] S3. Design a Mayer wave filter based on the obtained filter band, and take the designed Mayer wave filter coefficient as the initial filter coefficient of the MCKD.

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

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

[0110] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application, and not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the application.

Claims

1. A bearing fault diagnosis method of optimized filtered MCKD, characterized in that, Comprising: S1. Collecting a vibration signal, and performing Fourier transform on the frequency spectrum of the vibration signal to obtain a key function; S2. Setting an initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a spectrum trend, and dividing different bandwidths and center frequencies of filter bands based on the minimum value of the trend spectrum as the boundary of spectrum segmentation, increasing the truncation length, repeating the above steps, and recording all results; The specific method for setting the initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a spectrum trend, and dividing different bandwidths and center frequencies of filter bands based on the minimum value of the trend spectrum as the boundary of spectrum segmentation is: Key function Inverse Fourier transform of a portion of the header, set initial truncation length , use New sequence representing the first number of the key function and the subsequent values are set to zero, the spectral trend as shown in equation 3: Equation 3; According to the spectrum trend, a trend spectrum is obtained, and different bandwidths and center frequencies of filter bands are divided based on the minimum value of the trend spectrum; S3. Designing a Mayer wavelet filter based on the obtained filter band, and taking the designed Mayer wavelet filter coefficient as the initial filter coefficient of the MCKD; S4. Calculating the MCKD filtering results of different initial filter coefficients, quantifying the fault characteristics of all filtered signals by the harmonic spectrum kurtosis HSK, selecting the filtering result corresponding to the maximum value of HSK as the global optimal solution of the MCKD, and completing fault diagnosis; Specifically: After selecting the filtering result corresponding to the maximum value of HSK, performing spectrum analysis and envelope demodulation on the selected filtering result to complete fault diagnosis.

2. The method of claim 1, wherein, The specific method for collecting a vibration signal and performing Fourier transform on the frequency spectrum of the vibration signal to obtain a key function is: Performing Fourier transform on the vibration signal to obtain a frequency spectrum, and performing Fourier transform on the frequency spectrum to obtain a key function.

3. The method of claim 2, wherein, The specific method for performing Fourier transform on the vibration signal to obtain a frequency spectrum is: The collected vibration signal is The frequency spectrum is obtained by Fourier transform of the formula 1. Formula 1 ; spectrum of the absolute values is a discrete sequence containing only real parts.

4. The method of claim 3, wherein, The specific method for performing Fourier transform on the frequency spectrum to obtain a key function is: Let , L be the sequence length, be a discrete non-negative sequence, the Fourier transform function of is shown in equation 2: Formula 2: The Fourier transform is performed on the amplitude sequence of the spectrum, and the calculated contains a real part and an imaginary part, is a key function for the signal .

5. The method of claim 1, wherein, Step S2 further comprises: increasing the truncation length, repeating the division of the spectrum, recording all results, and constructing a multi-layer tower type spectrum segmentation framework.

6. A bearing fault diagnostic system that optimizes filtered MCKD, characterized by, Comprising: A signal processing module for collecting a vibration signal and performing Fourier transform on the frequency spectrum of the vibration signal to obtain a key function; A spectrum segmentation module for setting an initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a spectrum trend, and dividing different bandwidths and center frequencies of filter bands based on the minimum value of the trend spectrum as the boundary of spectrum segmentation, increasing the truncation length, repeating the above steps, and recording all results; The specific method for setting the initial truncation length, performing inverse Fourier transform on the key function based on the initial truncation length to obtain a spectrum trend, and dividing different bandwidths and center frequencies of filter bands based on the minimum value of the trend spectrum as the boundary of spectrum segmentation is: Key function Inverse Fourier transform of a portion of the header, set initial truncation length , use New sequence representing the first number of the key function and zeroing the subsequent values, spectral trend as in equation 3: Equation 3; According to the spectrum trend, a trend spectrum is obtained, and different bandwidths and center frequencies of filter bands are divided based on the minimum value of the trend spectrum; A filter construction module for designing a Mayer wavelet filter based on the obtained filter band, and taking the designed Mayer wavelet filter coefficient as the initial filter coefficient of the MCKD; The fault diagnosis module is used for calculating MCKD filtering results of different initial filter coefficients, quantifying fault characteristics of all filtering signals by harmonic spectrum kurtosis (HSK), selecting a filtering result corresponding to a maximum value of HSK as a global optimal solution of MCKD, and completing fault diagnosis. After the filtering result corresponding to the maximum value of HSK is selected, spectrum analysis and envelope demodulation are performed on the selected filtering result, and fault diagnosis is completed.

7. An electronic device, comprising: Comprise: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to implement the steps of the bearing fault diagnosis method of optimized filtering MCKD as claimed in any one of claims 1 to 5.

8. A storage medium, characterized by a memory arranged to store computer executable instructions that, when executed, cause the processor to implement the steps of the bearing fault diagnosis method of optimized filtering MCKD as claimed in any one of claims 1 to 5.

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