Rolling bearing fault diagnosis method and system based on product envelope spectrum optimization diagram

Abstract

The invention discloses a rolling bearing fault diagnosis method and system based on a product envelope spectrum optimization diagram, and the method comprises the steps: obtaining a vibration signal of a rolling bearing, carrying out the cyclic spectrum coherence analysis, and obtaining a normalized vibration signal cyclic spectrum coherence value; correcting and adjusting the normalized cyclic spectrum coherence value of the vibration signal to obtain a fault signal-to-noise ratio index of a corrected envelope spectrum; and sorting and analyzing the fault signal-to-noise ratio indexes of the corrected envelope spectrum, and determining the fault of the rolling bearing. The method can improve the diagnosis precision and robustness of the rolling bearing fault under the conditions of low signal-to-noise ratio and complex interference. The rolling bearing fault diagnosis method and system based on the product envelope spectrum optimization diagram can be widely applied to the technical field of rolling bearing fault diagnosis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rolling bearing fault diagnosis, and particularly relates to a rolling bearing fault diagnosis method and system based on a product envelope spectrum optimization graph. BACKGROUND

[0002] Rolling bearings are key components in the operation of mechanical equipment, and their operating state directly affects the performance and safety of the entire rotating mechanical system. However, the complex internal structure of the rotating mechanical system and the harsh working environment often lead to frequent bearing failures, thereby affecting the performance and causing accidents. Therefore, monitoring the bearing state and effectively identifying the fault are crucial to ensure the high reliability and optimal operating performance of the equipment.

[0003] Envelope analysis is one of the most mature methods, which is the benchmark for bearing fault diagnosis. Despite its widespread use, the fault information related to bearing faults is often masked by interference and noise, making the isolation of fault features and the removal of unnecessary interference a challenging task. High-frequency resonance demodulation, which analyzes the envelope spectrum (ES) of the signal filtered within the resonance frequency band, has become a popular method for extracting fault patterns from complex signals. In order to adaptively identify the fault-related resonance frequency band, the spectral kurtosis and its fast algorithm Kurtogram have been proposed. However, Kurtogram is sensitive to random impacts, making it difficult to distinguish between fault-induced components and random impact components. In order to address these limitations, several improved methods have been developed, and more advanced selection indicators such as the correlation kurtosis of ES, Gini coefficient, and envelope harmonic-to-noise ratio have been introduced for optimal signal selection.

[0004] In recent years, analysis based on cyclostationarity has been proven to be another promising method for bearing fault diagnosis, as bearing fault signals often exhibit cyclostationarity. In the cyclostationarity-based method, the cyclic spectral coherence (CSCoh) is usually introduced to reveal the fault features, the enhanced ES (EES) is generated by integrating over the entire spectral band, and the improved ES (IES) is generated by summing over a specific narrow band of the CSCoh. However, the manual selection of the frequency band of IES requires professional knowledge, which may lead to uncertain diagnostic results in practical applications. This has prompted research into adaptive fault information frequency band selection for IES, and L2 / L1 norm, frequency domain signal-to-noise ratio, and cyclostationary negative entropy have further improved fault information frequency band selection. However, the increasingly complex mechanical structure and more severe operating environment of Industry 4.0 have led to an increase in interference components in the monitored signals. For example, electromagnetic interference generated by magnetic induction cutting and non-fault rotating mechanical interference such as blade passing frequency appear in the sensor monitoring signals, and these signal features are very similar to fault features, which severely affect the discrimination of the above indicators. Therefore, reliable extraction of fault information under strong interference and heavy noise remains a challenge. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide a method and system for diagnosing rolling bearing faults based on product envelope spectrum optimization diagrams, which can improve the diagnostic accuracy and robustness of rolling bearing faults under low signal-to-noise ratio and complex interference conditions.

[0006] The first technical solution adopted in this invention is: a rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram, comprising the following steps: The vibration signal of the rolling bearing was acquired, and cyclic spectrum coherence analysis was performed to obtain the normalized cyclic spectrum coherence value of the vibration signal. The normalized vibration signal cyclic spectrum coherence value is corrected and adjusted to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. The fault signal-to-noise ratio index of the modified envelope spectrum is sorted and analyzed to determine the fault of the rolling bearing.

[0007] Furthermore, the step of acquiring the vibration signal of the rolling bearing and performing cyclic spectrum coherence analysis to obtain the normalized cyclic spectrum coherence value of the vibration signal specifically includes: Vibration signals of rolling bearings are acquired using accelerometers, and the instantaneous autocorrelation function of the vibration signals is constructed. By performing a two-dimensional Fourier transform on the time and time delay of the instantaneous autocorrelation function, the cyclic spectrum coherence value of the vibration signal is obtained; The cyclic spectrum coherence value of the vibration signal is normalized to obtain the normalized cyclic spectrum coherence value of the vibration signal.

[0008] Furthermore, the expression for the instantaneous autocorrelation function of the vibration signal is as follows: ; In the above formula, The instantaneous autocorrelation function of the vibration signal. Indicates the modulation period of the vibration signal. This represents the statistical expectation operator. Indicates complex conjugation. Indicates vibration signal, Indicates time, Indicates time delay.

[0009] Furthermore, the specific expression for performing a two-dimensional Fourier transform on the time and time delay of the instantaneous autocorrelation function is as follows: ; In the above formula, Indicates the coherence value of the cyclic spectrum. The instantaneous autocorrelation function of the vibration signal. Indicates time, Indicates time delay. Indicates the cycle frequency. Indicates the frequency spectrum.

[0010] Furthermore, the step of correcting and adjusting the coherence value of the normalized vibration signal cyclic spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum specifically includes: Based on the frequency range of the spectrum, the normalized cyclic spectrum coherence value of the vibration signal is divided into several sub-bands, and the improved envelope spectrum corresponding to the several sub-bands is obtained. By mitigating the adverse effects of interference similar to fault characteristics on the improved envelope spectra corresponding to several sub-bands, a corrected envelope spectrum is obtained. The fault characteristic information of different spectral bands in the corrected envelope spectrum is estimated to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum.

[0011] Furthermore, the specific expression for calculating the modified envelope spectrum is as follows: ; In the above formula, Indicates the modified envelope spectrum. Indicates the characteristic frequency of bearing failure. This indicates an improved envelope spectrum. Indicates the cycle frequency. Indicates the frequency spectrum. Indicates the cycle frequency Subtract bearing failure characteristic frequency Improved envelope spectrum value at the location, Indicates the harmonic sequence number. An abbreviation for Fault.

[0012] Furthermore, the specific expression for calculating the fault signal-to-noise ratio index is as follows: ; In the above formula, Indicates the fault signal-to-noise ratio. This represents the number of harmonics at the fault characteristic frequency of interest. This indicates the characteristic frequency deviation that is taken into account for bearing slippage. Indicates the modified envelope spectrum. Indicates the characteristic frequency of bearing failure. Indicates the harmonic sequence number.

[0013] Furthermore, the step of ranking and analyzing the fault signal-to-noise ratio index of the modified envelope spectrum to determine the fault of the rolling bearing specifically includes: The fault signal-to-noise ratio index of the modified envelope spectrum is sorted according to the descending order sorting rule. The top K modified envelope spectra with the highest modified signal-to-noise ratio values ​​are selected for product fusion to obtain the product cyclic spectrum. By selecting the theoretical fault characteristic frequency, calculating the corrected signal-to-noise ratio of the product cyclic spectrum and maximizing it, the optimal product cyclic spectrum is obtained. The frequency components in the optimal product cycle spectrum are analyzed, and the failure of the rolling bearing is diagnosed based on the fault characteristic frequency.

[0014] Furthermore, the specific expression for calculating the optimal product cyclic spectrum is as follows: ; In the above formula, Represents the optimal product cyclic spectrum. This represents the corrected signal-to-noise ratio for the cyclic spectrum of products of all different orders. Indicates the first Cyclic spectrum of factorial product.

[0015] The second technical solution adopted in this invention is: a rolling bearing fault diagnosis system based on a product envelope spectrum optimization diagram, comprising: The first module is used to acquire the vibration signal of the rolling bearing and perform cyclic spectrum coherence analysis to obtain the normalized cyclic spectrum coherence value of the vibration signal. The second module is used to correct and adjust the coherence value of the normalized vibration signal cyclic spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. The third module is used to sort and analyze the fault signal-to-noise ratio index of the modified envelope spectrum to determine the fault of the rolling bearing.

[0016] The beneficial effects of the method and system of this invention are as follows: This invention acquires the vibration signal of a rolling bearing and performs cyclic spectrum coherence analysis to obtain the normalized vibration signal cyclic spectrum coherence value. Further, the normalized vibration signal cyclic spectrum coherence value is corrected and adjusted to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. Based on the concept of product cyclic spectrum, the optimal parameters are adaptively selected at the inflection point of the index change, and multiple improved envelope spectra are used to enhance fault characteristics, achieving adaptive screening of fault frequency bands. Finally, the fault signal-to-noise ratio index of the corrected envelope spectrum is sorted and analyzed to determine the fault of the rolling bearing. By introducing the corrected signal-to-noise ratio index to evaluate fault information in different frequency ranges of the cyclic bispectral graph, the problem of indistinguishable fault components and interference in the cyclic bispectral graph is effectively solved. Attached Figure Description

[0017] Figure 1 This is a flowchart of the steps of the rolling bearing fault diagnosis method based on the product envelope spectrum optimization diagram of the present invention; Figure 2This is a structural block diagram of the rolling bearing fault diagnosis system based on the product envelope spectrum optimization diagram of the present invention; Figure 3 This is a schematic diagram of real-time vibration signal acquisition provided in a specific embodiment of the present invention; Figure 4 This is a schematic diagram of the envelope spectrum of the original signal provided in a specific embodiment of the present invention; Figure 5 This is a schematic diagram of cyclic bispectral data provided in a specific embodiment of the present invention; Figure 6 This is a schematic diagram of the corrected signal-to-noise ratio of all different orders of product cyclic spectra provided in a specific embodiment of the present invention; Figure 7 This is a schematic diagram of the corrected signal-to-noise ratio index values ​​of all corrected envelope spectra provided in specific embodiments of the present invention; Figure 8 This is a schematic diagram of the optimal product cyclic spectrum provided by a specific embodiment of the present invention. Detailed Implementation

[0018] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. The step numbers in the following embodiments are only for ease of explanation and do not limit the order of the steps. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.

[0019] This invention significantly suppresses interference components by constructing a modified envelope spectrum and proposes the concept of a product cyclic spectrum. By adaptively selecting optimal parameters at the inflection point of index changes, it achieves intelligent selection of effective frequency bands. The aim is to improve the diagnostic accuracy and robustness of rolling bearing faults under low signal-to-noise ratio and complex interference conditions.

[0020] Reference Figure 1 This invention provides a method for diagnosing rolling bearing faults based on an optimized product envelope spectrum, the method comprising the following steps: S100. Acquire the vibration signal of the rolling bearing and perform cyclic spectrum coherence analysis to obtain the normalized cyclic spectrum coherence value of the vibration signal. S110. Obtain the vibration signal of the rolling bearing through an accelerometer and construct the instantaneous autocorrelation function of the vibration signal; In this embodiment, vibration signal acquisition is performed by using a vibration acceleration sensor installed in the bearing housing or an adjacent structure to collect the vibration signal of the rolling bearing during operation. The collected vibration signal is denoted as... ,in, This indicates the number of sample points collected.

[0021] Furthermore, for a set of bearing vibration signals Its instantaneous autocorrelation function can be described as: ; In the above formula, The instantaneous autocorrelation function of the vibration signal. Indicates the modulation period of the vibration signal. This represents the statistical expectation operator. Indicates complex conjugation. Indicates vibration signal, Indicates time, Indicates time delay.

[0022] S120. By performing a two-dimensional Fourier transform on the time and time delay of the instantaneous autocorrelation function, the cyclic spectrum coherence value of the vibration signal is obtained. Cyclic spectral coherence can be obtained by adjusting time. and latency The instantaneous autocorrelation function is obtained by performing a two-dimensional Fourier transform, as shown below: ; In the above formula, Indicates the coherence value of the cyclic spectrum. The instantaneous autocorrelation function of the vibration signal. Indicates time, Indicates time delay. Indicates the cycle frequency. Indicates the frequency spectrum.

[0023] S130. Normalize the cyclic spectrum coherence value of the vibration signal to obtain the normalized cyclic spectrum coherence value of the vibration signal.

[0024] In this embodiment, normalization is performed to enhance the ability to characterize the coherence between the two frequency components, and its expression is: ; in The value ranges from 0 to 1, representing the normalized strength of energy coupling between different frequency components.

[0025] S200. Correct and adjust the coherence value of the normalized vibration signal cyclic spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. S210. Based on the frequency range of the spectrum, the normalized cyclic spectrum coherence value of the vibration signal is divided into several sub-bands to obtain the improved envelope spectrum corresponding to the several sub-bands. In this embodiment, the cyclic spectrum of each frequency band is corrected, and the fault information contained therein is evaluated.

[0026] Cyclic spectral coherence analysis divides the frequency range of the spectrum into M sub-bands. Each sub-band can be viewed as an improved envelope spectrum (IES), expressed as follows: ; in , The spectrum bandwidth is defined and calculated as follows: ; In the above formula, Indicates the spectrum bandwidth.

[0027] S220. By mitigating the adverse effects of interference similar to fault characteristics on the improved envelope spectra corresponding to several sub-bands, a corrected envelope spectrum is obtained. In this embodiment, the adverse effects of interference similar to fault characteristics are mitigated by adjusting each improved envelope spectrum, thereby obtaining the modified envelope spectrum (MIES), the expression of which is: ; In the above formula, Indicates the modified envelope spectrum. Indicates the characteristic frequency of bearing failure. This indicates an improved envelope spectrum. Indicates the cycle frequency. Indicates the frequency spectrum. Indicates the cycle frequency Subtract bearing failure characteristic frequency Improved envelope spectrum value at the location, Indicates the harmonic sequence number (i.e., a positive integer, such as 1, 2, 3, ...). An abbreviation for Fault.

[0028] S230. Estimate the fault characteristic information of different spectral bands in the corrected envelope spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum.

[0029] In this embodiment, the fault signal-to-noise ratio (SNR) index for each corrected envelope spectrum is calculated to estimate the richness of fault feature information in different spectral bands. The formula for calculating the corrected SNR is as follows: ; In the above formula, Indicates the fault signal-to-noise ratio. This represents the number of harmonics at the fault characteristic frequency of interest. This indicates the characteristic frequency deviation that is taken into account for bearing slippage. Indicates the modified envelope spectrum. Indicates the characteristic frequency of bearing failure. This represents the harmonic index (i.e., a positive integer, such as 1, 2, 3, ..., 0).

[0030] S300. The fault signal-to-noise ratio index of the modified envelope spectrum is sorted and analyzed to determine the fault of the rolling bearing.

[0031] S310. Sort the fault signal-to-noise ratio index of the modified envelope spectrum according to the descending sorting rule, select the top K modified envelope spectra with the highest modified signal-to-noise ratio values ​​for product fusion, and obtain the product cyclic spectrum. In this embodiment, the product cyclic spectrum is constructed, and the corrected signal-to-noise ratio of each corrected envelope spectrum is arranged in descending order, with the expression being: ; The top K corrected envelope spectra with the highest corrected signal-to-noise ratio values ​​are selected for product fusion, and the expression is as follows: ; In the above formula, , .

[0032] S320. Select the theoretical fault characteristic frequency, calculate the corrected signal-to-noise ratio of the product cyclic spectrum and maximize it to obtain the optimal product cyclic spectrum. S330. Analyze the frequency components in the optimal product cycle spectrum and diagnose the rolling bearing fault based on the fault characteristic frequency.

[0033] In this embodiment, the theoretical fault characteristic frequency is selected and the corrected signal-to-noise ratio of all different order product cyclic spectra is calculated. The expression is as follows: ; The optimal product cyclic spectrum is determined by maximizing the corrected signal-to-noise ratio of all product cyclic spectra, and its expression is as follows: ; In the above formula, Represents the optimal product cyclic spectrum. This represents the corrected signal-to-noise ratio for the cyclic spectrum of products of all different orders. Indicates the first Cyclic spectrum of factorial product.

[0034] Finally, the frequency components in the optimal product cycle spectrum are analyzed, and the faults of rotating machinery are diagnosed based on the fault characteristic frequencies.

[0035] In summary, the embodiments of the present invention have the following advantages over the prior art: 1) Strong anti-interference capability: The introduction of a modified signal-to-noise ratio index to evaluate fault information in different frequency ranges of the cyclic bispectral graph effectively solves the problem that fault components and interference cannot be distinguished in the cyclic bispectral graph.

[0036] 2) Multi-band fusion: The adaptive product cyclic spectrum method aims to enhance fault features by using multiple improved envelope spectra, thereby effectively solving the problem that fault features cannot be extracted from a single frequency band in complex signals due to the weak fault information in that band.

[0037] 3) Adaptive Optimization: Based on the characteristic that the improved envelope spectrum containing fault information will cause the modified signal-to-noise ratio index to continuously increase during the product process, the idea of ​​product cyclic spectrum is proposed. By adaptively selecting the optimal parameters at the inflection point of index change, the fault frequency band can be adaptively screened.

[0038] Finally, further explanation is provided with reference to the accompanying drawings of the embodiments of the present invention. This embodiment uses a vibration acceleration signal of a petrochemical circulating pump bearing with an outer ring fault, obtained by a vibration acceleration sensor, to illustrate the implementation process and diagnostic effect of the present invention, as detailed below: like Figure 3 As shown, vibration signals are collected in real time using a vibration sensor installed near the bearing housing. The sampling frequency is set to 51200Hz, the motor speed is 1171.8RPM, and the duration of each data collection is 1.28s.

[0039] like Figure 4 As shown, this is the envelope spectrum of the original signal. The red line in the figure represents the characteristic frequency of the bearing outer ring fault and its harmonics. It can be seen from the figure that there is no obvious spectral line corresponding to the bearing outer ring fault. In addition, there is a prominent spectral line at 153.9 Hz in the figure, which is the interference of non-fault components, affecting the fault identification.

[0040] like Figure 5 As shown, the cyclic bispectral graph obtained from cyclic spectral coherence analysis of the original signal reveals widespread interference information above 5kHz, while fault information is concentrated around 2kHz. The improved envelope spectrum for each frequency band is calculated and corrected. The amount of fault information contained in each corrected envelope spectrum is calculated using the corrected signal-to-noise ratio (SNR) index, and then sorted in descending order to further construct the product cyclic spectrum. The number of harmonics at the fault characteristic frequencies of interest is set when calculating the corrected SNR. The value is 3, used to account for the characteristic frequency deviation caused by bearing slippage. for ,in For spectral resolution, the total number K considered when constructing the product cyclic spectrum is set to 40.

[0041] like Figure 6 As shown, the corrected signal-to-noise ratios (SNRs) of the product cyclic spectra of all different orders are shown. The maximum value is obtained at order 4 (marked with a red asterisk), indicating that the corrected envelope spectra with the top 4 corrected SNRs contain fault information.

[0042] like Figure 7As shown, the corrected signal-to-noise ratio values ​​for all corrected envelope spectra are displayed. The top four frequency bands are marked with a red asterisk in the figure, representing the main resonant frequency bands of the bearing in the range of 1.9-2.2kHz.

[0043] like Figure 8 The figure shows the product cycle spectrum of order 4, i.e., the optimal product cycle spectrum. Figure 8 In the process, the characteristic frequencies of bearing outer ring failure can be clearly observed. and its harmonics and The spectral lines indicate a fault in the outer ring of the bearing, and the method proposed in this invention effectively diagnoses the bearing fault. These results strongly demonstrate the effectiveness of the method proposed in this invention for fault diagnosis of rotating machinery.

[0044] Reference Figure 2 A rolling bearing fault diagnosis system based on product envelope spectrum optimization diagram includes: The first module 201 is used to acquire the vibration signal of the rolling bearing and perform cyclic spectrum coherence analysis to obtain the normalized cyclic spectrum coherence value of the vibration signal. The second module 202 is used to correct and adjust the coherence value of the normalized vibration signal cyclic spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. The third module 203 is used to sort and analyze the fault signal-to-noise ratio index of the modified envelope spectrum to determine the fault of the rolling bearing.

[0045] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0046] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for fault diagnosis of rolling bearings based on product envelope spectrum optimization diagram, characterized in that, Includes the following steps: The vibration signal of the rolling bearing was acquired, and cyclic spectrum coherence analysis was performed to obtain the normalized cyclic spectrum coherence value of the vibration signal. The normalized vibration signal cyclic spectrum coherence value is corrected and adjusted to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. The fault signal-to-noise ratio index of the modified envelope spectrum is sorted and analyzed to determine the fault of the rolling bearing.

2. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 1, characterized in that, The step of acquiring the vibration signal of the rolling bearing and performing cyclic spectrum coherence analysis to obtain the normalized cyclic spectrum coherence value of the vibration signal specifically includes: Vibration signals of rolling bearings are acquired using accelerometers, and the instantaneous autocorrelation function of the vibration signals is constructed. By performing a two-dimensional Fourier transform on the time and time delay of the instantaneous autocorrelation function, the cyclic spectrum coherence value of the vibration signal is obtained; The cyclic spectrum coherence value of the vibration signal is normalized to obtain the normalized cyclic spectrum coherence value of the vibration signal.

3. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 2, characterized in that, The expression for the instantaneous autocorrelation function of the vibration signal is as follows: ； In the above formula, The instantaneous autocorrelation function of the vibration signal. Indicates the modulation period of the vibration signal. This represents the statistical expectation operator. Indicates complex conjugation. Indicates vibration signal, Indicates time, Indicates time delay.

4. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 3, characterized in that, The specific expression for performing a two-dimensional Fourier transform on the time and time delay of the instantaneous autocorrelation function is as follows: ； In the above formula, Indicates the coherence value of the cyclic spectrum. The instantaneous autocorrelation function of the vibration signal. Indicates time, Indicates time delay. Indicates the cycle frequency. Indicates the frequency spectrum.

5. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 4, characterized in that, The step of correcting and adjusting the coherence value of the normalized vibration signal cyclic spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum specifically includes: Based on the frequency range of the spectrum, the normalized cyclic spectrum coherence value of the vibration signal is divided into several sub-bands, and the improved envelope spectrum corresponding to the several sub-bands is obtained. By mitigating the adverse effects of interference similar to fault characteristics on the improved envelope spectra corresponding to several sub-bands, a corrected envelope spectrum is obtained. The fault characteristic information of different spectral bands in the corrected envelope spectrum is estimated to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum.

6. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 5, characterized in that, The specific expression for calculating the modified envelope spectrum is as follows: ； In the above formula, Indicates the modified envelope spectrum. Indicates the characteristic frequency of bearing failure. This indicates an improved envelope spectrum. Indicates the cycle frequency. Indicates the frequency spectrum. Indicates the cycle frequency Subtract bearing failure characteristic frequency Improved envelope spectrum value at the location, Indicates the harmonic sequence number. An abbreviation for Fault.

7. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 6, characterized in that, The specific formula for calculating the fault signal-to-noise ratio is as follows: ； In the above formula, Indicates the fault signal-to-noise ratio. This represents the number of harmonics at the fault characteristic frequency of interest. This indicates the characteristic frequency deviation that is taken into account for bearing slippage. Indicates the modified envelope spectrum. Indicates the characteristic frequency of bearing failure. Indicates the harmonic sequence number.

8. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 7, characterized in that, The step of ranking and analyzing the fault signal-to-noise ratio index of the modified envelope spectrum to determine the fault of the rolling bearing specifically includes: The fault signal-to-noise ratio index of the modified envelope spectrum is sorted according to the descending order sorting rule. The top K modified envelope spectra with the highest modified signal-to-noise ratio values ​​are selected for product fusion to obtain the product cyclic spectrum. By selecting the theoretical fault characteristic frequency, calculating the corrected signal-to-noise ratio of the product cyclic spectrum and maximizing it, the optimal product cyclic spectrum is obtained. The frequency components in the optimal product cycle spectrum are analyzed, and the failure of the rolling bearing is diagnosed based on the fault characteristic frequency.

9. The rolling bearing fault diagnosis method based on product envelope spectrum optimization diagram according to claim 8, characterized in that, The specific expression for calculating the optimal product cyclic spectrum is as follows: ； In the above formula, Represents the optimal product cyclic spectrum. This represents the corrected signal-to-noise ratio for the cyclic spectrum of products of all different orders. Indicates the first Cyclic spectrum of factorial product.

10. A rolling bearing fault diagnosis system based on product envelope spectrum optimization diagram, characterized in that, Includes the following modules: The first module is used to acquire the vibration signal of the rolling bearing and perform cyclic spectrum coherence analysis to obtain the normalized cyclic spectrum coherence value of the vibration signal. The second module is used to correct and adjust the coherence value of the normalized vibration signal cyclic spectrum to obtain the fault signal-to-noise ratio index of the corrected envelope spectrum. The third module is used to sort and analyze the fault signal-to-noise ratio index of the modified envelope spectrum to determine the fault of the rolling bearing.

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