Multichannel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and vector envelope analysis

Abstract

The invention provides a multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and vector envelope analysis, which comprises the following steps of: acquiring multi-channel acoustic emission signals under normal operation of a sliding bearing and different bearing bush damage states by installing acoustic emission signal sensors in two directions of the section of the sliding bearing; classifying the multi-channel acoustic emission signals to obtain a data set of the multi-channel acoustic emission signals under different working conditions; performing multivariate variational mode decomposition on the multi-channel acoustic emission signal to obtain a plurality of intrinsic mode function components in different directions, screening out the intrinsic mode function components greater than the correlation kurtosis of the original acoustic emission signal in each direction by taking the correlation kurtosis as a parameter index, and recombining the intrinsic mode function components into a new acoustic emission signal; and carrying out vector envelope analysis on the recombined acoustic emission signals, organically fusing the recombined acoustic emission signals in the two directions according to an elliptical orbit, and carrying out envelope analysis on the fused signals to obtain vector envelope spectrums in a normal state and different damage states, so as to form a database for judging the damage of the bearing bush of the sliding bearing. The acoustic emission signals of the sliding bearing to be judged are subjected to the same processing through the method, the vector envelope spectrum obtained through processing is compared with the image in the database, and then diagnosis of the damage state of the bearing bush of the sliding bearing to be judged is completed. The method effectively solves the problems that the single-direction sliding bearing bush fault diagnosis precision is low, and the result is not comprehensive.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical system fault diagnosis and signal processing, and in particular relates to a multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and total vector envelope analysis. Background Art

[0002] In the context of modern mass production, sliding bearings are one of the essential components of many mechanical equipment. As key supporting and lubricating components in heavy-duty machines such as large wind turbines and hydroelectric generators, they play a vital role. As the use time of sliding bearings increases, parts such as bearing bushes and internal support rings inside the bearings will be damaged. Therefore, it is of great significance to monitor the condition and diagnose faults of sliding bearings. Summary of the invention

[0003] The present invention proposes a multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and full vector envelope analysis. An acoustic emission signal sensor is used to collect multi-channel acoustic emission signals in two directions of the same cross section of the sliding bearing. The multi-channel acoustic emission signal is subjected to multivariate variational mode decomposition, and the intrinsic mode function components in the two directions obtained by decomposition are reorganized according to the size of the relevant kurtosis. The relevant kurtosis is used as a screening standard to obtain suitable intrinsic mode function components and reorganize them, that is, a relatively pure acoustic emission signal with noise removed. Then, the reorganized acoustic emission signals in the two directions are organically integrated using the full vector envelope spectrum, and the full vector envelope spectrum is obtained to form a database for judging the damage of the sliding bearing bush. The acoustic emission signal of the sliding bearing to be judged is processed in the same way by the proposed method, and the processed full vector envelope spectrum is compared with the image in the database, so as to diagnose the damage state of the sliding bearing bush to be judged. The accurate identification of the damage condition of the sliding bearing bush is completed by analyzing the full vector envelope spectrum.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] (a) Acoustic emission signal sensors are installed in two directions of the cross section of the sliding bearing. The two acoustic emission signal sensors are located in different directions of the same cross section, and the two are 90 degrees apart. The acoustic emission sensor is connected to a data acquisition card, and the data acquisition card transmits data to a computer through a USB interface, wherein the sampling frequency is 1000kHz and the continuous sampling time is 100 seconds. Acoustic emission signals of sliding bearings with different fault degrees under normal operation and different bearing damage states are collected, and the obtained acoustic emission signals are classified to obtain an initial data set including acoustic emission signals under normal operation and different bearing damage states;

[0006] (b) The acoustic emission signals of the normal state and different bearing damage states are divided into signals, and the number of signal points in each segment is 1e7. The acoustic emission signals are subjected to multivariate variational modal decomposition to obtain multiple intrinsic mode function components in each direction. The intrinsic mode function components with a value greater than the relevant kurtosis of the original acoustic emission signal in each direction are screened out with the relevant kurtosis as the parameter index, and they are recombined into new acoustic emission signals. The parameters of the mode number and penalty factor in the multivariate variational modal decomposition are 2 and 1200, respectively.

[0007] (c) Performing full vector envelope analysis on the new acoustic emission signal, organically fusing the acoustic emission signals in two directions through elliptical trajectories, that is, first performing Hilbert transform on the acoustic emission signals in two directions, then fusing the obtained envelope signals, and finally obtaining the final full vector envelope spectrum based on the main vibration vector of the fused signal, respectively obtaining full vector envelope spectrum images under normal state and different bearing damage states, and then obtaining a database for sliding bearing bearing damage diagnosis;

[0008] (d) The acoustic emission signal of the sliding bearing to be diagnosed is processed in the same way, and the processed full vector envelope spectrum is compared with the image in the database to complete the judgment of the damage state of the sliding bearing bushing to be diagnosed.

[0009] As a preferred technical solution, in step (a), before collecting the acoustic emission signal data of each fault state, first collect the acoustic emission signal data of the normal bearing operation state, and then collect the bearing damage data according to the three bearing damage states of damage, 2mm scratch, and 4mm scratch in turn; after collecting the normal data and the three types of fault data, pre-process the data, including: preliminary denoising of the acoustic emission signal; retaining five decimal places for the acoustic emission signal;

[0010] As a preferred technical solution, in step (b), the multi-channel acoustic emission signal is subjected to multivariate variational mode decomposition, the correlation kurtosis of each component in the intrinsic mode function component obtained by decomposition of each channel is calculated respectively, and the average correlation kurtosis is obtained, and each intrinsic mode function component with a correlation kurtosis higher than the average in each channel is screened, and the components are reorganized into a new multi-channel acoustic emission signal, which has a more obvious periodic impact than the original acoustic emission signal and a higher effective information component;

[0011] As a preferred technical solution, in step (c), the reorganized multi-channel acoustic emission signal is subjected to full vector envelope analysis, the signal is first subjected to Hilbert transform to obtain envelope signals of two channels, the envelope signals of the two channels are combined into a complex signal, and the sequence of the complex signal is decomposed into a main vibration vector, a secondary vibration vector and a phase according to the motion trajectory of the ellipse, and the main vibration vector is expanded along the frequency axis to obtain a full vector envelope spectrum;

[0012] As a preferred technical solution, in step (c), the full vector envelope spectra of the normal state and the different bearing damage states are saved in the same folder in sequence according to the different states to form a database of different damage states of the sliding bearing bearings;

[0013] As a preferred technical solution, in step (d), the same multivariate variational modal decomposition and full vector envelope analysis method is used to collect and analyze the signal of the sliding bearing to be diagnosed, and the obtained full vector envelope spectrum is compared with the image in the database, so as to make a relatively accurate judgment on the bearing condition of the sliding bearing to be diagnosed.

[0014] By adopting the above technical solution, the beneficial effects of the present invention are as follows:

[0015] The present invention adopts a multi-channel signal decomposition algorithm of multivariate variational mode decomposition, which can synchronously decompose the signals of two channels into multiple intrinsic mode function components in two directions according to the same constraints, and decompose the non-stationary and nonlinear signals into relatively stable sinusoidal signals while ensuring the decomposition accuracy;

[0016] The present invention proposes to use correlation kurtosis as an evaluation index for signal screening. By calculating the correlation kurtosis and average correlation kurtosis of multiple intrinsic mode functions in two directions, multiple intrinsic mode function components with higher than the average correlation kurtosis are screened and reorganized, which can reduce residual noise, increase the proportion of effective components, and enhance the impact characteristics of the signal.

[0017] The present invention adopts full vector envelope analysis as the fusion analysis algorithm of multi-channel signals, decomposes the envelope signals obtained after Hilbert transform of acoustic emission signals in two directions according to the motion trajectory of the ellipse, and then organically combines the signals of the two channels into one signal. At the same time, Hilbert transform further improves the stability of the signal, and improves the accuracy of the signal while ensuring that the calculation results are relatively balanced and comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention 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 embodiments described herein are only used to illustrate and explain the invention and are not used to limit the present invention.

[0019] Figure 1 is a flow chart of a fault diagnosis method constructed according to a preferred embodiment of the present invention;

[0020] Figure 2 It is a time domain diagram of a sliding bearing bush with mild damage collected according to a preferred embodiment of the present invention;

[0021] Figure 3It is a three-dimensional time domain diagram of the intrinsic mode function after multivariate variational mode decomposition according to the preferred embodiment of the present invention.

[0022] Figure 4 It is the total vector envelope spectrum obtained after total vector envelope analysis. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] The terms of this embodiment are explained, including: Multivariate Variational Mode Decomposition (MVMD), Intrinsic Mode Function (IMF).

[0025] like Figure 1 The flowchart of the multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and full vector envelope analysis constructed according to the preferred embodiment of the present invention is shown. The method includes the following steps:

[0026] Step 1: Collect multi-channel acoustic emission signals of the sliding bearing in normal operation and under different bearing damage conditions at a sampling frequency of 1000kHz. Preprocess each acoustic emission signal, including denoising the acoustic emission signal and rounding off the five decimal places after the decimal point of the acoustic emission signal. The time domain diagram of the processed inner race fault acoustic emission signal is shown in Figure 1. Figure 2 shown.

[0027] Step 2: Perform multivariate variational mode decomposition on the acoustic emission signal to obtain several eigenmode function graphs in two directions. The eigenmode functions are as follows: Figure 3 shown.

[0028] Step 3: Calculate the relevant kurtosis value and average relevant kurtosis value of each eigenmode function in two directions respectively, screen and reorganize the eigenmode function components with a value higher than the average relevant kurtosis value, and obtain the recombined acoustic emission signals in two directions.

[0029] Step 4: Perform full vector envelope analysis on the reorganized acoustic emission signal, organically fuse the acoustic emission signals in two directions through elliptical trajectories, and finally obtain the final full vector envelope spectrum based on the main vibration vector of the fused signal, such as Figure 4 shown.

[0030] Step 5: Construct full vector envelope spectrum images under normal conditions and different bearing damage conditions, and then obtain a database for sliding bearing bearing damage diagnosis.

[0031] Step 6: Collect the multi-channel acoustic emission signal of the current sliding bearing, and use the methods in the above steps 1, 2, 3, and 4 to obtain the full vector envelope spectrum of the sliding bearing bushing to be diagnosed, and complete the judgment of the damage to the sliding bearing bushing to be diagnosed by comparing it with the database constructed in step 5.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and full vector envelope analysis, characterized in that: The following steps are involved: (a) Acoustic emission signal sensors are installed in two directions on the cross section of the sliding bearing to collect acoustic emission signals of the sliding bearing in normal operation and at different fault levels under different bearing damage conditions, and the acquired acoustic emission signals are classified to obtain an initial data set including acoustic emission signals in normal operation and at different bearing damage conditions; (b) performing multivariate variational mode decomposition on the acoustic emission signal to obtain multiple intrinsic mode function components in each direction, using the correlation kurtosis as a parameter index, screening out the intrinsic mode function components in each direction that are greater than the correlation kurtosis of the original acoustic emission signal, and recombining them into a new acoustic emission signal; (c) Performing full vector envelope analysis on the new acoustic emission signal, and organically fusing the acoustic emission signals in two directions through elliptical trajectories, that is, first performing Hilbert transform on the acoustic emission signals in two directions, then fusing the obtained envelope signals, and finally obtaining the final full vector envelope spectrum based on the main vibration vector of the fused signal, thereby completing the noise reduction and processing of the acoustic emission signals in normal state and under different bearing damage conditions; (d) The acoustic emission signal of the sliding bearing to be tested is collected and processed according to the above steps, and the obtained full vector envelope spectrum and the full vector envelope spectrum of the normal state and different damage states constitute a database for sliding bearing bushing damage diagnosis. (e) The acoustic emission signal of the sliding bearing to be diagnosed is processed in the same way, and the processed full vector envelope spectrum is compared with the image in the database to complete the judgment of the damage state of the sliding bearing bushing to be diagnosed.

2. The multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and total vector envelope analysis as claimed in claim 1 is characterized in that: In step (a), after collecting the acoustic emission signals of the sliding bearing under normal operation and under different bearing damage conditions, the data is preprocessed, including: preliminary denoising of the acoustic emission signals; retaining four decimal places for the acoustic emission signals.

3. The multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and total vector envelope analysis as claimed in claim 1 is characterized in that: In step (c), the acoustic emission signal is subjected to full vector envelope analysis. The multi-channel envelope signal obtained after Hilbert transformation is defined as the main vibration vector and amplitude vibration vector according to the major and minor axes of the elliptical trajectory, and the final full vector envelope spectrum is obtained through the main vibration vector.

4. The multi-channel sliding bearing acoustic emission signal fault diagnosis method based on multivariate variational mode decomposition and total vector envelope analysis as claimed in claim 1 is characterized in that: In step (d), the damage condition of the sliding bearing bush is determined by comparing the amplitude and frequency in the full vector envelope spectrum obtained from the main vibration vector, especially the medium and high frequency segments.

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