CN117109923A - Rolling bearing fault diagnosis method and system - Google Patents
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Abstract
The invention relates to a rolling bearing fault diagnosis method and system, and belongs to the technical field of mechanical fault diagnosis. Firstly, collecting vibration signals of a rolling bearing; carrying out signal decomposition on the vibration signal by adopting an ICEEMDAN algorithm to obtain IMF components and residual error remainder, and carrying out reconstruction on each IMF component by integrating coefficient indexes such as energy entropy, kurtosis, cross-correlation coefficient and the like; according to the spectrum kurtosis method, a rapid kurtosis diagram is obtained on the reconstructed signal, band-pass filtering is carried out on the reconstructed signal after filtering, hilbert envelope is carried out on the reconstructed signal after filtering, and the envelope spectrum of the corresponding reconstructed signal is obtained through rapid Fourier transformation; and comparing the fault characteristic frequency obtained by calculating the rolling bearing through a theoretical formula with the frequency at the position with a larger envelope spectrum peak value, and determining the fault characteristic state of the rolling bearing so as to diagnose the fault of the rolling bearing. The invention reconstructs the decomposed IMF component by comprehensive indexes such as comprehensive energy entropy, so that the bearing fault characteristic frequency and the frequency doubling peak value in the envelope spectrum are more obvious, and the diagnosis effect is better.
Description
Technical Field
The invention relates to a rolling bearing fault diagnosis method and system, and belongs to the technical field of mechanical fault diagnosis.
Background
Rolling bearings are used as key supporting parts in important machines such as gas turbines, aeroengines and the like, and are easy to generate various faults such as abrasion, pitting corrosion and the like when frequently operated under severe environments with high load, high frequency and high temperature. In order to avoid huge economic losses and even casualties caused by faults of the rolling bearing, accurate fault diagnosis of the rolling bearing is necessary.
The existing rolling bearing fault diagnosis method is mainly a time-frequency domain analysis method, for example, a rolling bearing fault diagnosis method based on local feature scale decomposition is disclosed in application publication No. CN114923689A, a rolling bearing vibration signal is decomposed and reconstructed through the local feature scale decomposition and an effective ISC component, fault information of the rolling bearing is extracted, and whether the bearing breaks down or not is judged by comparing with a fault theoretical value. However, the problems of weak fault signal characteristics and nonlinearity of the rolling bearing are still outstanding when the signals are decomposed, and effective fault characteristic information of the rolling bearing is difficult to accurately extract by adopting the algorithm, so that a fault diagnosis result is not ideal.
Disclosure of Invention
The invention aims to provide a rolling bearing fault diagnosis method and system, which are used for solving the problems of error and non-ideal diagnosis effect of the existing fault diagnosis result.
The invention provides a rolling bearing fault diagnosis method for solving the technical problems, which comprises the following steps:
1) Collecting vibration signals of the rolling bearing;
2) ICEEMDAN decomposition is carried out on the acquired vibration signals to obtain IMF components;
3) Selecting IMF components according to comprehensive indexes to reconstruct to obtain a reconstructed signal, wherein the comprehensive indexes comprise energy entropy, kurtosis and cross-correlation coefficients;
4) And processing the reconstructed signal to obtain an envelope spectrum of the reconstructed signal, and determining the fault characteristic state of the rolling bearing according to the envelope spectrum.
According to the invention, firstly, vibration signals of the rolling bearing are collected, then the vibration signals are decomposed to obtain IMF components, IMF components are selected according to comprehensive indexes including energy entropy for reconstruction, envelope spectrum of the reconstructed signals is obtained by processing the reconstructed signals, and finally, fault characteristic states of the rolling bearing are determined through the envelope spectrum. The invention reconstructs the decomposed IMF component by comprehensive indexes such as comprehensive energy entropy, so that the bearing fault characteristic frequency and the frequency doubling peak value in the envelope spectrum are more obvious, and the diagnosis effect is better.
Further, the step of selecting the IMF component according to the synthesis index is: and calculating an energy entropy value, a kurtosis value and a cross-correlation coefficient between each IMF component and an original signal after vibration signal decomposition, respectively setting an energy entropy threshold value, a kurtosis threshold value and a cross-correlation coefficient threshold value, taking the IMF component with the energy entropy value larger than the energy entropy threshold value as a first effective IMF component, the IMF component with the kurtosis value larger than the kurtosis threshold value as a second effective IMF component, taking the IMF component with the cross-correlation coefficient larger than the cross-correlation coefficient threshold value as a third effective IMF component, and selecting a final effective IMF component according to the first effective IMF component, the second effective IMF component and the third effective IMF component.
Further, the final effective IMF component is an IMF component that satisfies the energy entropy threshold, the kurtosis threshold, and the cross-correlation coefficient threshold simultaneously.
The reconstruction of the IMF component by the comprehensive index can well reduce the interference of noise in the vibration signal and improve the signal-to-noise ratio so as to effectively solve the problems of weak and nonlinear fault signal characteristics of the rolling bearing.
Further, comparing the frequency of the larger peak value in the envelope spectrum of the reconstruction signal with the fault characteristic frequency calculated by the theoretical formula to determine the fault characteristic state of the rolling bearing.
Further, the step 4) obtains a kurtosis diagram of the reconstructed signal according to a spectral kurtosis method, selects a frequency bandwidth and a center frequency corresponding to the position with the largest kurtosis in the kurtosis diagram, and carries out band-pass filtering on the reconstructed signal by using the frequency bandwidth and the center frequency as band-pass filter parameters, and carries out time-frequency conversion on the reconstructed signal after filtering to obtain an envelope spectrum.
The spectral kurtosis is very sensitive to transient impact hidden in noise, and the parameters of the band-pass filter can be automatically determined by using the spectral kurtosis method, so that the fault of the rolling bearing is better diagnosed.
Further, the vibration signal is collected by a vibration acceleration sensor.
A rolling bearing fault diagnosis system comprises a signal extraction module, a signal processing module and a fault diagnosis module; the signal extraction module is used for collecting vibration signals of the rolling bearing; the signal processing module is used for decomposing the vibration signal to obtain an IMF component, selecting the IMF component according to the comprehensive index, reconstructing the IMF component to obtain a reconstructed signal, and finally processing the reconstructed signal to obtain an envelope spectrum of the reconstructed signal; the fault diagnosis module is used for determining the fault characteristic state of the rolling bearing according to the envelope spectrum of the reconstructed signal.
According to the invention, firstly, vibration signals of the rolling bearing are collected, then the vibration signals are decomposed to obtain IMF components, IMF components are selected according to comprehensive indexes including energy entropy for reconstruction, envelope spectrum of the reconstructed signals is obtained by processing the reconstructed signals, and finally, fault characteristic states of the rolling bearing are determined through the envelope spectrum. The invention reconstructs the decomposed IMF component by comprehensive indexes such as comprehensive energy entropy, so that the bearing fault characteristic frequency and the frequency doubling peak value in the envelope spectrum are more obvious, and the diagnosis effect is better.
Drawings
FIG. 1 is a flow chart diagram of a method for diagnosing a rolling bearing failure in accordance with the present invention;
FIG. 2 is a time domain diagram of IMF components after ICEEMDAN decomposition in accordance with an embodiment of the present invention;
FIG. 3 is a time domain diagram of a reconstructed signal after consideration of a synthesis index in an embodiment of the present invention;
FIG. 4 is a graph of the rapid kurtosis of a reconstructed signal in an embodiment of the invention;
FIG. 5 is a Hilbert envelope spectrum of a filtered reconstructed signal in an embodiment of the invention;
fig. 6 is a diagnostic interface diagram of a rolling bearing failure diagnosis system in an embodiment of the invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings.
Embodiments of a Rolling bearing failure diagnosis System
The system comprises a signal extraction module, a signal processing module and a fault diagnosis module; the signal extraction module is used for collecting vibration signals of the rolling bearing; the signal processing module is used for decomposing an original vibration signal to obtain an IMF component and a residual error remainder based on an ICEEMDAN algorithm, selecting an effective IMF component for reconstruction according to a comprehensive index by setting the comprehensive index to comprise threshold evaluation of energy entropy, kurtosis and cross-correlation coefficient, and carrying out band-pass filtering on the reconstructed signal based on spectrum kurtosis to obtain a Hilbert envelope spectrum; the fault diagnosis module is used for determining the fault characteristic state of the rolling bearing according to the envelope spectrum of the reconstructed signal.
The implementation flow of the system is shown in fig. 1, and is described below with reference to a specific example.
And collecting fault original vibration signals of the outer ring of the rolling bearing through a vibration acceleration sensor. In the embodiment, the rolling bearing model is 6205-2RS SKF, the damage diameter of the outer ring of the bearing is 0.007 inch through electric spark machining single-point damage for fault simulation, the original vibration signal is acquired under the working condition that no load exists and the motor rotating speed is 1797r/min, and the sampling frequency of the vibration sensor is 12000Hz.
The time domain diagram of each IMF component after the decomposition of icemdan is shown in fig. 2, and the original vibration signal is decomposed by the icemdan: ICEEMDAN takes the difference between the residual error of the last iteration and the average value of the residual error containing noise as the IMF of the iteration, and carries out multiple decomposition and reconstruction on the vibration signal; define x as the signal to be decomposed, E k (. Cndot.) represents the k-th order IMF component produced by EMD decomposition, N (. Cndot.) represents the local mean, w, of the resulting signal (i) Representing gaussian noise; adding group I white noise w to original vibration signal sequence (i) Construct sequence x (i) ＝x+β o E(w (i) ) The first set of residuals is obtained as:
R 1 ＝(N(x (i) ))
the first modal component may be calculated as:
d 1 ＝x-R 1
continuing to add noise, and calculating a second group of residual errors by utilizing local mean decomposition as follows:
R 1 +β 0 E(w (i) )
the second modal component is:
d 2 ＝R 1 -R 2 ＝R 1 -(N(R 1 +β 1 E(w (i) )))
the kth residual can thus be calculated as:
R k ＝(N(R k-1 +β k-1 E(w (i) )))
the K-th modal component is:
d k ＝R k-1 -R k
according to the above formula, all IMF components and residual terms are obtained, the fault characteristic information contained in the original vibration signal after multiple decomposition is basically concentrated in the first few IMF components, in this embodiment, only the processes of the first eight IMF components are considered for subsequent signal reconstruction, and after the first eight IMF components of the original signal decomposition are obtained, the sampling frequency f is selected s The corresponding time t is determined by the time sequence of the original signal data of the frequency-domain signal of 12000Hz and the length n of the original signal data sequence, so that a time domain diagram of the relation between the first eight IMF component sequences and the time can be depicted, fault information submerged by noise is difficult to find in the decomposed first eight IMF component time domain diagrams, and corresponding outer ring fault characteristic information is also required to be found through subsequent further processing.
As shown in fig. 3, the eight IMF components decomposed by icemdan are filtered and reconstructed by adopting comprehensive indexes including energy entropy, kurtosis, cross-correlation coefficient and the like, and the energy entropy is defined as:
wherein p (x) i ) Indicating that the random time X is X i Probability of (2);
the definition of kurtosis value K is:
wherein mu and sigma are respectively the mean value and standard deviation of the signal x, and E (t) represents the expected value of the variable t;
defining the cross-correlation coefficient of each decomposed IMF component and the original signal as follows:
in the method, in the process of the invention,for cross-correlation of each IMF component with the original signal, R s (τ) is the autocorrelation of the original signal;
the method for comprehensively screening the effective IMF component by using three indexes of energy entropy, kurtosis and cross-correlation coefficient comprises the following steps:
(1) Setting an energy entropy threshold value to be 0.1, calculating the energy entropy value of each IMF component after ICEEMDAN decomposition, comparing the energy entropy value with the threshold value, and screening out an effective IMF component as a first effective IMF component by taking the energy entropy as an evaluation index;
(2) The vibration signal of the normal bearing approximately obeys normal distribution, the kurtosis value is about 3, a kurtosis threshold kurtosis=3.2 is set, the kurtosis values of eight INF components are calculated, and the effective IMF components are evaluated and screened as second effective IMF components by using a kurtosis value index;
(3) Judging authenticity of the IMF components through cross-correlation coefficients between the IMF components and the original signals after ICEEMDAN decomposition to screen effective IMF components, setting a cross-correlation coefficient threshold value rho=0.03, and respectively calculating cross-correlation coefficients between the IMF components and the original signals to compare with the threshold value so as to screen the effective IMF components as third effective IMF components;
(4) And (5) integrating indexes such as energy entropy, kurtosis, cross-correlation coefficient and the like, screening out effective IMF components, and reconstructing to obtain a reconstructed signal. The final effective IMF component is an IMF component that satisfies the energy entropy threshold, the kurtosis threshold, and the cross-correlation coefficient threshold simultaneously.
The filtered IMF component is reconstructed to obtain a reconstructed signal, and the sampling frequency f is selected s =12000 Hz and reconstruction signal data sequence length n 1 Thereby determining the corresponding time t 1 A time domain plot of the reconstructed signal sequence versus time is thus depicted.
As shown in fig. 4, the reconstructed signal is subjected to band-pass filtering by a spectral kurtosis method to obtain a rapid kurtosis graph of the reconstructed signal; the general vibration signal model of the rolling bearing can be expressed by the following formula:
Z(t)＝X(t)+N(t)
z (t) is the vibration signal actually measured, X (t) is the fault signal detected, and N (t) is the noise signal; potential faults of the rolling bearing generate a series of repeated transient impact forces, so as to excite certain structural resonances of the system, and a reasonable general model of X (t) is as follows:
h (t) is the impulse response caused by a single impact, X k And τ k Respectively representing the random amplitude and the occurrence time of each pulse; the definition given for the spectral kurtosis is: the kurtosis value calculated at the frequency f of the output of the ideal filter bank is the spectral kurtosis. The method can obtain the following steps:
wherein K is X (f) The spectral kurtosis of X (t), ρ (f) is the signal-to-noise ratio, i.eIt is a function of frequency, S N (f) And S is X (f) The power spectral densities of noise and signal, respectively; at frequencies where the signal-to-noise ratio is very high, K Z (f) Approximately equal to K X (f) At frequencies where noise is intense, K Z (f) Tending to a zero value; so that the spectral kurtosis method can scrutinize the entire frequency domain, looking for those frequency bands where the fault signal can be best detected.
The rapid kurtosis graph adopted in the embodiment is calculated based on a tower algorithm, the calculation time is obviously shortened compared with other methods, and the rapid kurtosis graph is used as a blind identification tool of a detection filter for fault diagnosis, so that parameters of a band-pass filter can be determined in a self-adaptive mode; the abscissa of the rapid kurtosis graph represents the frequency f, the ordinate represents the number of decomposition layers K, and the sampling frequency of the signal is f s And Δf=f s ·2 -(K+1) The color shade on the image represents the spectral kurtosis value at each f and Δf; and selecting a frequency bandwidth and a center frequency corresponding to the maximum kurtosis in the rapid kurtosis graph, knowing that the frequency bandwidth is 750Hz and the center frequency is 3375Hz, and carrying out band-pass filtering on the reconstruction signal by using the frequency bandwidth and the center frequency as band-pass filter parameters.
As shown in fig. 5, the Hilbert transform (Hilbert) is performed on the filtered reconstructed signal to obtain a corresponding resolved signal, and the modulus of the resolved signal is calculated to obtain an envelope signal, where the Hilbert transform is an output after the signal passes through an all-pass filter with an amplitude of 1, and a continuous time signal is set as x (t), where the definition of the Hilbert transform is as follows:
in the method, in the process of the invention,is the output of the filter with unit impulse +.>
The obtained envelope signal is subjected to Fast Fourier Transform (FFT) to obtain a Hilbert envelope spectrum; the fault vibration signal of the rolling bearing generally has a relatively significant amplitude characteristic, and a Fast Fourier Transform (FFT) can quickly realize the transformation of the signal from a time domain to a frequency domain, so as to obtain a Hilbert envelope spectrum from the envelope signal. Since the acquired original vibration signal has discrete characteristics, the acquired signal is processed using a Discrete Fourier Transform (DFT). The DFT calculation formula of the finite length discrete signal x (n) is:
where k=0, 1,..n-1; i is an imaginary unit, and N is a sampling point number; n=0, 1 ". . ....，N-1；
Comparing the fault characteristic frequency calculated by a theoretical formula with the frequency at the position with a larger envelope spectrum peak value, taking a rolling bearing outer ring fault frequency calculation formula as an example:
wherein n is the rotation speed of the main shaft, D is the diameter of the rolling bodies, D is the pitch diameter of the rolling bearing, alpha is the contact angle, and z is the number of the rolling bodies;
the failure frequency of the outer ring of the rolling bearing is calculated to be 107.2Hz by a theoretical formula, the maximum frequency corresponding to the peak value in the actual envelope spectrum is 107.7Hz, and the frequency multiplication of the failure characteristic frequency of the outer ring in the envelope spectrum can be obviously found, namely the failure of the outer ring of the rolling bearing can be determined, so that the failure diagnosis of the rolling bearing is realized.
As shown in FIG. 6, after the system is opened, the system can complete the functions of displaying geometric parameters of the rolling bearing, calculating characteristic frequency, calculating time domain indexes, drawing time domain waveform diagrams, drawing frequency spectrograms, drawing time-frequency domain analysis wavelet transformation components and fault diagnosis results, drawing EMD decomposition modal functions and reconstruction envelope spectrums, EEMD decomposition modal functions and reconstruction envelope spectrums, VMD decomposition components and reconstruction envelope spectrums, drawing EMD improved spectrum kurtosis quick kurtosis map, drawing envelope spectrums, drawing ICEEEMDAN improved spectrum kurtosis quick kurtosis map, drawing envelope spectrums and the like by only clicking required functions, finally providing the functions of clearing data and exiting software, and bringing great convenience to the fault analysis of other subsequent working conditions of the rolling bearing so as to realize fault diagnosis with higher quality. The system operation is based on data driving, and the occupied memory is small. The system is designed based on an object-oriented programming method, has strong portability and can realize the expansion of functions.
Embodiments of a method for diagnosing faults of a Rolling bearing
According to the invention, firstly, vibration signals of the rolling bearing are collected, then IMF components and residual errors remainder are obtained based on an ICEEMDAN algorithm and the original vibration signal signals are decomposed, effective IMF components are selected for reconstruction according to comprehensive indexes through setting threshold evaluation including energy entropy, kurtosis and cross-correlation coefficients, band-pass filtering is carried out on reconstruction signals based on spectrum kurtosis, so that Hilbert envelope spectrum is obtained, and fault characteristic states of the rolling bearing are determined according to the envelope spectrum. The specific implementation of the method is described in detail in the embodiments of the rolling bearing fault diagnosis system, and will not be described here again.
It will be appreciated by persons skilled in the art that the foregoing description is a preferred embodiment of the invention, and is not intended to limit the invention, but rather to limit the invention to the specific embodiments described, and that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for elements thereof, for the purposes of those skilled in the art. Modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. A method for diagnosing a rolling bearing failure, comprising the steps of:
1) Collecting vibration signals of the rolling bearing;
2) ICEEMDAN decomposition is carried out on the acquired vibration signals to obtain IMF components;
3) Selecting IMF components according to comprehensive indexes to reconstruct to obtain a reconstructed signal, wherein the comprehensive indexes comprise energy entropy, kurtosis and cross-correlation coefficients;
4) And processing the reconstructed signal to obtain an envelope spectrum of the reconstructed signal, and determining the fault characteristic state of the rolling bearing according to the envelope spectrum.
2. The rolling bearing fault diagnosis method according to claim 1, wherein the step of selecting the IMF component based on the comprehensive index is: and calculating an energy entropy value, a kurtosis value and a cross-correlation coefficient between each IMF component and an original signal after vibration signal decomposition, respectively setting an energy entropy threshold value, a kurtosis threshold value and a cross-correlation coefficient threshold value, taking the IMF component with the energy entropy value larger than the energy entropy threshold value as a first effective IMF component, the IMF component with the kurtosis value larger than the kurtosis threshold value as a second effective IMF component, taking the IMF component with the cross-correlation coefficient larger than the cross-correlation coefficient threshold value as a third effective IMF component, and selecting a final effective IMF component according to the first effective IMF component, the second effective IMF component and the third effective IMF component.
3. The rolling bearing fault diagnosis method according to claim 2, wherein the final effective IMF component is an IMF component satisfying an energy entropy threshold, a kurtosis threshold, and a cross correlation coefficient threshold at the same time.
4. The rolling bearing fault diagnosis method according to claim 1, wherein the frequency at which the peak value in the envelope spectrum of the reconstructed signal is large is compared with the fault characteristic frequency calculated by the theoretical formula, and the rolling bearing fault characteristic state is determined.
5. The method for diagnosing the fault of the rolling bearing according to claim 1, wherein the step 4) is to calculate the kurtosis map of the reconstructed signal according to the spectral kurtosis method, select the frequency bandwidth and the center frequency corresponding to the position with the largest kurtosis in the kurtosis map, perform band-pass filtering on the reconstructed signal by using the frequency bandwidth and the center frequency as band-pass filter parameters, and obtain the envelope spectrum after performing time-frequency conversion on the reconstructed signal after filtering.
6. The method for diagnosing a rolling bearing failure according to claim 1, wherein said vibration signal is collected by a vibration acceleration sensor.
7. The rolling bearing fault diagnosis system is characterized by comprising a signal extraction module, a signal processing module and a fault diagnosis module; the signal extraction module is used for collecting vibration signals of the rolling bearing; the signal processing module and the fault diagnosis module adopt the rolling bearing fault diagnosis method described in 1-6 for diagnosis.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117629636A (en) * | 2023-12-05 | 2024-03-01 | 哈尔滨工程大学 | Health assessment and fault diagnosis method and system for rolling bearing of combustion engine |
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| CN117629636A (en) * | 2023-12-05 | 2024-03-01 | 哈尔滨工程大学 | Health assessment and fault diagnosis method and system for rolling bearing of combustion engine |
| CN117629636B (en) * | 2023-12-05 | 2024-05-24 | 哈尔滨工程大学 | Health assessment and fault diagnosis method and system for rolling bearing of combustion engine |
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