Rolling bearing multichannel signal fault diagnosis method and related equipment

Abstract

The invention provides a multi-channel signal fault diagnosis method for a rolling bearing and related equipment. The multi-channel signal fault diagnosis method comprises the following steps: acquiring an original multi-channel vibration signal of a target rolling bearing; performing decomposition and noise reduction on the original multi-channel vibration signal through self-adaptive multivariable characteristic mode decomposition to obtain a multi-channel intrinsic mode component; multi-channel comprehensive indexes of the decomposed multi-channel intrinsic mode components are calculated, and the multi-channel intrinsic mode component with the maximum multi-channel comprehensive index is screened out; and analyzing the screened multi-channel intrinsic mode component through a vector square envelope spectrum, thereby performing fault diagnosis on the rolling bearing and obtaining a rolling bearing fault diagnosis result. Compared with the prior art, the fault characteristic frequency of the rolling bearing can be accurately extracted under the interference of the rotation frequency and the frequency multiplication of the rotation frequency.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a rolling bearing multi-channel signal fault diagnosis method and related equipment. Background Art

[0002] Rolling bearings are one of the most critical and most prone to failure parts in the mechanical system of industrial equipment. They are under high load for a long time. When a rolling bearing fails, it may cause an accident or even huge economic losses. Therefore, monitoring its operating status and fault diagnosis is an important task in modern industry. Common rolling bearing failures include inner ring failure, rolling element failure, and outer ring failure.

[0003] At present, traditional rolling bearing fault diagnosis methods have achieved good diagnostic results to a certain extent and have made progress. For example, the application of modal decomposition method and envelope spectrum technology in rolling bearing fault diagnosis has achieved many significant results, and the fault diagnosis method of rolling bearing multi-channel signals that combines multivariable modal decomposition method with holographic spectrum, full spectrum and full vector spectrum has also begun to attract the attention of many scholars.

[0004] Traditional rolling bearing fault diagnosis methods are mainly based on signal processing methods of single-channel vibration signals, such as frequency domain analysis, time domain analysis, wavelet analysis, etc.; the method of analyzing and diagnosing the vibration signal of a single-channel rolling bearing through spectrum and envelope spectrum is widely used. However, in the actual working condition of the rolling bearing, there may be a lot of noise, and due to the different locations of the vibration sensor, the collected vibration signal may also have a certain degree of difference, and the information contained in the multi-channel vibration signal of the rolling bearing is often richer. Therefore, it is difficult to detect the operating state of the rolling bearing and accurately determine the fault type of the rolling bearing only by the time-frequency domain analysis method of the single-channel vibration signal of the rolling bearing. Summary of the invention

[0005] The present invention provides a rolling bearing multi-channel signal fault diagnosis method and related equipment, the purpose of which is to improve the accuracy of rolling bearing fault diagnosis.

[0006] To achieve the above object, the present invention provides a rolling bearing multi-channel signal fault diagnosis method, comprising:

[0007] Step 1, collecting the original multi-channel vibration signal of the target rolling bearing, and preprocessing the original multi-channel vibration signal;

[0008] Step 2, decomposing and denoising the original multi-channel vibration signal by adaptive multivariate eigenmode decomposition to obtain multi-channel intrinsic mode components;

[0009] Step 3, calculating the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components, and screening out the multi-channel intrinsic mode components with the largest multi-channel comprehensive index;

[0010] Step 4: Analyze the selected multi-channel intrinsic mode components through the full vector square envelope spectrum, so as to diagnose the rolling bearing fault and obtain the rolling bearing fault diagnosis result.

[0011] More specifically, step 1 includes:

[0012] Vibration sensors are arranged in different directions of the rolling bearing to collect its original multi-channel vibration signals;

[0013] The collected data are preprocessed by rounding them off to retain four significant figures.

[0014] More specifically, adaptive multivariate mode decomposition includes:

[0015] Input multi-channel signal, namely:

[0016] X(n)=[x1(n),x2(n),…,x o (n)],n=1,2,…,N

[0017] Among them, x o (n) represents the oth channel signal;

[0018] Set the basic parameters of adaptive multivariate mode decomposition (f s , C n , I), determine the parameters (L, K) of adaptive multivariate modal decomposition through the improved zebra optimization algorithm, f s is the sampling frequency, L is the filter length, C n is the shearing number of the oth channel signal, K is the maximum number of modes extracted, and I is the maximum number of iterations;

[0019] Define the initial iteration i=1 and establish an adaptive FIR filter bank. For the signal of each channel, use M Hanning windows to construct a set of uniformly distributed adaptive FIR filters.

[0020] Get the decomposed multi-channel modal components, namely:

[0021]

[0022] in, represents the mth modal component of the oth channel signal decomposition at the i-th iteration; X o Represents a matrix of M rows with the same filter length L; represents the mth FIR filter of the oth channel signal at the i-th iteration, m=1,2,…,M; Represents the convolution operator symbol.

[0023] Estimate fault period and update filter coefficients, autocorrelation spectrum is defined as follows:

[0024]

[0025] Multivariate eigenmode decomposition to determine fault cycles using autocorrelation spectra Then, based on the estimated failure period and the above decomposed modal components Update the filter coefficients.

[0026] Determine whether the maximum number of iterations I has been reached. If i = I, terminate the iteration process and proceed to the next step; otherwise, return to the decomposition process.

[0027] Select the final multi-channel modal decomposition components, calculate the correlation coefficient CC between all two adjacent multi-channel modal components; construct a multi-channel correlation coefficient matrix MCC (M×M×o) and lock the two multi-channel modal components with the largest correlation coefficients; calculate the correlation kurtosis of the two multi-channel modal components; remove the multi-channel modal components with smaller correlation kurtosis and set M=M-1.

[0028] Specifically, the formula for calculating the correlation coefficient is as follows:

[0029]

[0030] CC m× ( m-1 ) ×o represents the correlation coefficient between the mth and m-1th modal components of the oth channel, and They represent the means of the mth and m-1th modal components of the oth channel respectively.

[0031] When the maximum number of modes K is reached, the iteration process is terminated and the final multivariate signal decomposition result is output. (k) The mathematical expression of (n) is as follows:

[0032]

[0033] in represents the kth decomposed modal component of the oth channel.

[0034] To go further, the multi-channel composite index calculation formula in step 3 is as follows:

[0035]

[0036] in and They are The kurtosis and characteristic energy ratio of the kth modal component in the oth channel; N o represents the total number of channels of the multivariate signal; N represents the number of data of the multivariate signal; σ is the standard deviation of the kth modal component; represents the mean value of the kth decomposed modal component of the oth channel; f represents the fault characteristic frequency; A(f), A(2f), A(3f) are the amplitudes of the first three fault characteristic frequencies at the envelope spectrum; A total Represents the total amplitude of the envelope spectrum.

[0037] The present invention also provides a rolling bearing multi-channel signal fault diagnosis device, comprising:

[0038] An acquisition module is used to acquire multi-channel original vibration signals of the target rolling bearing in normal working state and in different fault states;

[0039] A decomposition module, used for decomposing the multi-channel original vibration signal by adaptive multivariate eigenmode decomposition to obtain multi-channel intrinsic mode components;

[0040] A screening module is used to calculate the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components and use it as an evaluation index to screen out the optimal multi-channel intrinsic mode components;

[0041] The diagnosis module is used to generate the full vector square envelope spectrum of the multi-channel intrinsic mode components for fault diagnosis and obtain the fault diagnosis result of the target rolling bearing.

[0042] The above scheme of the present invention has the following beneficial effects:

[0043] The present invention collects the original multi-channel vibration signal of the target rolling bearing and pre-processes the original multi-channel vibration signal; decomposes and reduces the noise of the original multi-channel vibration signal through adaptive multivariable characteristic mode decomposition to obtain multiple multi-channel intrinsic mode components; calculates the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components, and screens out the multi-channel intrinsic mode components with the largest multi-channel comprehensive index; analyzes the screened multi-channel intrinsic mode components through the full vector square envelope spectrum, thereby diagnosing the rolling bearing fault and obtaining the rolling bearing fault diagnosis result; compared with the prior art, the adaptive multi-variable characteristic mode decomposition can adaptively determine parameters to avoid the problem of manually setting parameters; the full vector square envelope spectrum can enhance the readability of the spectrum and can effectively distinguish the rotation frequency multiplication and the fault characteristic frequency, thereby improving the accuracy of fault diagnosis.

[0044] Other beneficial effects of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of a flow chart of an embodiment of the present invention;

[0046] Figure 2 It is a result diagram of adaptive multivariate eigenmode decomposition in an embodiment of the present invention and full vector envelope spectrum in the prior art;

[0047] Figure 3 It is a result diagram of adaptive multivariate eigenmode decomposition and full vector square envelope spectrum in an embodiment of the present invention; DETAILED DESCRIPTION

[0048] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than 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.

[0049] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] In view of the existing problems, the present invention provides a rolling bearing multi-channel signal fault diagnosis method and related equipment.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a rolling bearing multi-channel signal fault diagnosis method, comprising:

[0053] Step 1, collecting the original multi-channel vibration signal of the target rolling bearing, and preprocessing the original multi-channel vibration signal;

[0054] Step 2, decomposing and denoising the original multi-channel vibration signal by adaptive multivariate eigenmode decomposition to obtain multi-channel intrinsic mode components;

[0055] Step 3, calculating the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components, and screening out the multi-channel intrinsic mode components with the largest multi-channel comprehensive index;

[0056] Step 4: Analyze the selected multi-channel intrinsic mode components through the full vector square envelope spectrum, so as to diagnose the rolling bearing fault and obtain the rolling bearing fault diagnosis result.

[0057] Specifically, the embodiment of the present invention collects original vibration signals of the rolling bearing in the "upward" and "forward" directions under normal conditions, inner ring fault, rolling element fault, outer ring fault, and combined fault conditions through vibration sensors.

[0058] Specifically, step 1 includes:

[0059] Vibration sensors are arranged in different directions of the rolling bearing to collect its original multi-channel vibration signals;

[0060] The collected data are preprocessed by rounding them off to retain four significant figures.

[0061] Specifically, adaptive multivariate mode decomposition includes:

[0062] Input multi-channel signal, namely:

[0063] X(n)=[x1(n),x2(n),…,x o (n)],n=1,2,…,N

[0064] Among them, x o (n) represents the oth channel signal;

[0065] Set the basic parameters of adaptive multivariate mode decomposition (f s , C n , I), determine the parameters (L, K) of adaptive multivariate modal decomposition through the improved zebra optimization algorithm, f s is the sampling frequency, L is the filter length, C n is the shearing number of the oth channel signal, K is the maximum number of modes extracted, and I is the maximum number of iterations;

[0066] Define the initial iteration i=1 and establish an adaptive FIR filter bank. For the signal of each channel, use M Hanning windows to construct a set of uniformly distributed adaptive FIR filters.

[0067] Get the decomposed multi-channel modal components, namely:

[0068]

[0069] in, represents the mth modal component of the oth channel signal decomposition at the i-th iteration; X o Represents a matrix of M rows with the same filter length L; represents the mth FIR filter of the oth channel signal at the i-th iteration, m=1,2,…,M; Represents the convolution operator symbol.

[0070] Estimate fault period and update filter coefficients, autocorrelation spectrum is defined as follows:

[0071]

[0072] Multivariate eigenmode decomposition to determine fault cycles using autocorrelation spectra Then, based on the estimated failure period and the above decomposed modal components Update the filter coefficients.

[0073] Determine whether the maximum number of iterations I has been reached. If i = I, terminate the iteration process and proceed to the next step; otherwise, return to the decomposition process.

[0074] Select the final multi-channel modal decomposition components, calculate the correlation coefficient CC between all two adjacent multi-channel modal components; construct a multi-channel correlation coefficient matrix MCC (M×M×o) and lock the two multi-channel modal components with the largest correlation coefficients; calculate the correlation kurtosis of the two multi-channel modal components; remove the multi-channel modal components with smaller correlation kurtosis and set M=M-1.

[0075] Specifically, the formula for calculating the correlation coefficient is as follows:

[0076]

[0077] CC m× ( m-1 ) ×o represents the correlation coefficient between the mth and m-1th modal components of the oth channel, and They represent the means of the mth and m-1th modal components of the oth channel respectively.

[0078] When the maximum number of modes K is reached, the iteration process is terminated and the final multivariate signal decomposition result is output. (k) The mathematical expression of (n) is as follows:

[0079]

[0080] in represents the kth decomposed modal component of the oth channel.

[0081] Specifically, the multi-channel comprehensive index calculation formula in step 3 is as follows:

[0082]

[0083] in and They are The kurtosis and characteristic energy ratio of the kth modal component in the oth channel; N o represents the total number of channels of the multivariate signal; N represents the number of data of the multivariate signal; σ is the standard deviation of the kth modal component; represents the mean value of the kth decomposed modal component of the oth channel; f represents the fault characteristic frequency; A(f), A(2f), A(3f) are the amplitudes of the first three fault characteristic frequencies at the envelope spectrum; A total Represents the total amplitude of the envelope spectrum.

[0084] Specifically, after screening the multi-channel intrinsic mode components obtained by adaptive multivariate eigenmode decomposition through the multi-channel comprehensive index in step 3, the results of analyzing the optimal multi-channel intrinsic mode components through the full vector square envelope spectrum are as follows: Figure 2 To make the experimental results more convincing, the present invention uses the full vector envelope spectrum to analyze the same data. The results are shown in Figure 3 As shown, from Figure 2 and Figure 3 It can be seen that the full vector square envelope spectrum in the embodiment of the present invention can extract the 5th frequency of the rotation frequency (i.e. 5f r ) can also accurately extract the fault characteristic frequency f i However, the existing full vector envelope spectrum can only extract the 5th frequency of the rotation frequency, and cannot accurately extract the fault characteristic frequency.

[0085] The embodiment of the present invention collects the original multi-channel vibration signal of the target rolling bearing; decomposes and reduces the noise of the original multi-channel vibration signal through adaptive multivariable eigenmode decomposition to obtain multi-channel intrinsic mode components; calculates the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components, and screens out the multi-channel intrinsic mode components with the largest multi-channel comprehensive index; analyzes the screened multi-channel intrinsic mode components through the full vector square envelope spectrum, thereby diagnosing the rolling bearing fault and obtaining the rolling bearing fault diagnosis result; compared with the prior art, the adaptive multivariable eigenmode decomposition can adaptively determine parameters to avoid the problem of manually setting parameters; the full vector square envelope spectrum can enhance the readability of the spectrum and can effectively distinguish between the rotation frequency multiplication and the fault characteristic frequency, thereby improving the accuracy of fault diagnosis.

[0086] The present invention also provides a rolling bearing multi-channel signal fault diagnosis device, comprising:

[0087] An acquisition module is used to acquire multi-channel original vibration signals of the target rolling bearing in normal working state and in different fault states;

[0088] A decomposition module, used for decomposing the multi-channel original vibration signal by adaptive multivariate eigenmode decomposition to obtain multi-channel intrinsic mode components;

[0089] A screening module is used to calculate the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components and use it as an evaluation index to screen out the optimal multi-channel intrinsic mode components;

[0090] The diagnosis module is used to generate the full vector square envelope spectrum of the multi-channel intrinsic mode components for fault diagnosis and obtain the fault diagnosis result of the target rolling bearing.

[0091] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the embodiment of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0092] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0093] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A rolling bearing multi-channel signal fault diagnosis method, characterized in that: include: Step 1, collecting the original multi-channel vibration signal of the target rolling bearing, and preprocessing the original multi-channel vibration signal; Step 2, decomposing and denoising the original multi-channel vibration signal by adaptive multivariate eigenmode decomposition to obtain multi-channel intrinsic mode components; Step 3, calculating the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components, and screening out the multi-channel intrinsic mode components with the largest multi-channel comprehensive index; Step 4: Analyze the selected multi-channel intrinsic mode components through the full vector square envelope spectrum, so as to diagnose the rolling bearing fault and obtain the rolling bearing fault diagnosis result.

2. The rolling bearing multi-channel signal fault diagnosis method according to claim 1, characterized in that: The adaptive eigenmode decomposition comprises: Input multi-channel signal, namely: X(n)=[x1(n),x2(n),…,x o (n)],n=1,2,…,N Among them, x o (n) represents the oth channel signal; Set the basic parameters of adaptive multivariate mode decomposition (f s , C n , I), determine the parameters (L, K) of adaptive multivariate modal decomposition through the improved zebra optimization algorithm, f s is the sampling frequency, L is the filter length, C n is the shearing number of the oth channel signal, K is the maximum number of modes extracted, and I is the maximum number of iterations; Define the initial iteration i=1 and establish an adaptive FIR filter bank. For the signal of each channel, use M Hanning windows to construct a set of uniformly distributed adaptive FIR filters. Get the decomposed multi-channel modal components, namely: in, represents the mth modal component of the oth channel signal decomposition at the i-th iteration; X o Represents a matrix of M rows with the same filter length L; represents the mth FIR filter of the oth channel signal at the i-th iteration, m=1,2,…,M; Represents the convolution operator symbol. Estimate the fault period and update the filter coefficients, definition of the autocorrelation spectrum as follows: Multivariate eigenmode decomposition to determine fault cycles using autocorrelation spectra Then, based on the estimated failure period and the above decomposed modal components Update the filter coefficients. Determine whether the maximum number of iterations I has been reached. If i = I, terminate the iteration process and proceed to the next step; otherwise, return to the decomposition process. Select the final multi-channel modal decomposition components, calculate the correlation coefficient CC between all two adjacent multi-channel modal components; construct a multi-channel correlation coefficient matrix MCC (M×M×o) and lock the two multi-channel modal components with the largest correlation coefficients; calculate the correlation kurtosis of the two multi-channel modal components; remove the multi-channel modal components with smaller correlation kurtosis and set M=M-1. Specifically, the formula for calculating the correlation coefficient is as follows: CC m× ( m-1 ) ×o represents the correlation coefficient between the mth and m-1th modal components of the oth channel, and They represent the means of the mth and m-1th modal components of the oth channel respectively. When the maximum number of modes K is reached, the iteration process is terminated and the final multivariate signal decomposition result is output. (k) The mathematical expression of (n) is as follows: in represents the kth decomposed modal component of the oth channel.

3. The rolling bearing multi-channel signal fault diagnosis method according to claim 1, characterized in that: The expression of the multi-channel comprehensive index MCI is: in and They are The kurtosis and characteristic energy ratio of the kth modal component in the oth channel; N o represents the total number of channels of the multivariate signal; N represents the number of data of the multivariate signal; σ is the standard deviation of the kth modal component; represents the mean value of the kth decomposed modal component of the oth channel; f represents the fault characteristic frequency; A(f), A(2f), A(3f) are the amplitudes of the first three fault characteristic frequencies at the envelope spectrum; A total Represents the total amplitude of the envelope spectrum.

4. A rolling bearing multi-channel signal fault diagnosis device, characterized in that: include: An acquisition module is used to acquire multi-channel original vibration signals of the target rolling bearing in normal working state and in different fault states; A decomposition module, used for decomposing the multi-channel original vibration signal by adaptive multivariate eigenmode decomposition to obtain multi-channel intrinsic mode components; A screening module is used to calculate the multi-channel comprehensive index of the decomposed multi-channel intrinsic mode components and use it as an evaluation index to screen out the optimal multi-channel intrinsic mode components; The diagnosis module is used to generate the full vector square envelope spectrum of the multi-channel intrinsic mode components for fault diagnosis and obtain the fault diagnosis result of the target rolling bearing.

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