CN111079710A - Multilayer noise reduction method based on improved CEEMD rolling bearing signal - Google Patents
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Abstract
The invention discloses a multilayer noise reduction method based on an improved CEEMD rolling bearing signal, which comprehensively considers the influence of high-frequency components and noise signals contained in the rolling bearing on the training interference and error of a diagnosis prediction function during fault diagnosis of a rolling bearing. The method adopts endpoint truncation to solve the problem of overlarge endpoint curvature distortion, extracts high-frequency and noise signals by SVD singular value difference spectrum, determines the standard deviation of a group of added Gaussian white noise signals with symmetrical positive and negative signs opposite, determines the iterative aggregation times of decomposition in CEEMD, finally evaluates the linear correlation degree of each IMF component and the original signal, and removes low correlation quantity and irrelevant quantity. The method can be suitable for signal noise reduction optimization preprocessing of rolling bearing fault diagnosis, improves the noise reduction type limitation of single filtering, and simultaneously reduces the requirements on computer hardware.
Description
Technical Field
The invention relates to a multilayer noise reduction method, in particular to a multilayer noise reduction method based on improved CEEMD rolling bearing signals.
Background
When a mechanical device fails, the peak and effective values of the vibration parameters at sensitive points tend to change significantly or new vibration components appear. When the fault diagnosis is carried out on the machine, the data of amplitude domain, frequency domain, time domain and the like of the vibration signal can reflect the fault information of the machine. The vibration signal detection system can be used for timely finding and identifying abnormal vibration phenomena, and diagnosing whether or not and the degree of the mechanical equipment fails through the prediction of a neural network algorithm, so that the vibration is controlled and reduced, and serious safety production accidents are avoided. The vibration signal of the rolling bearing contains a large amount of running state information and is represented by a modulation signal which is not smooth and multi-component, a large amount of sudden changes and short-term impact components in a fault signal are also contained, and particularly, the fault characteristics are difficult to identify at the early stage of the fault due to the weak modulation source, the weak early fault signal and the noise interference of surrounding equipment. Therefore, the noise reduction and characteristic signal extraction method has important significance for the normal operation of the bearing equipment.
Singular Value Decomposition (SVD) is based on a reconstruction matrix and is used as a nonlinear filtering method, so that random noise components in signals can be effectively eliminated, periodic components in the signals can be extracted, and relatively pure fault signals can be obtained. However, the SVD decomposition of the large matrix of long data has high requirements on the solving performance of a computer and is prone to the problem of insufficient memory.
The CEEMD enables extreme points of the original signal to be distributed more uniformly by adding white noise, covers abnormal signals such as high-frequency intermittence or noise in the original signal and then is decomposed by the EMD, so that modal confusion can be weakened. However, the basic CEEMD requires manual experience to set parameters, so that the general applicability does not exist, the parameters need to be set according to experience for each different signal, and the parameter precision is not high.
Disclosure of Invention
The invention aims to solve the defects in the prior art, provides a reliable and effective multilayer noise reduction method based on improved CEEMD rolling bearing signals, enhances the self-adaptive improvement capability of CEEMD on different signals, and improves the comprehensive noise reduction capability on the original vibration signals of the rolling bearing.
The specific scheme of the invention is as follows: a multilayer noise reduction method based on improved CEEMD rolling bearing signals mainly comprises the following steps:
firstly, time definition processing is carried out on an original rolling bearing signal, the length of the rolling bearing signal to be analyzed is intercepted, a corresponding time point of each signal sampling point is defined, and the corresponding matching with actual sampling time, sampling duration and sampling frequency is carried out.
According to the length of a data chain for sampling the vibration signal of the rolling bearing, the data chain is adaptively decomposed into a plurality of column vectors of 2048 × 1, and the total decomposition number is as follows:
in the formula, fix () is rounding to zero in parentheses, and the iteration counter is: m is 1,2, …, M;
defining the amount of time for each data point as
In which i e [1,2048 ]]。
Step two: and according to the definition processing of the original rolling bearing signal, performing disassembly and assembly iteration operation on the long data chain of the rolling bearing vibration signal, disassembling the original data chain into a plurality of short data chains, and performing SVD decomposition and noise reduction.
Splitting each 2048 x 1 column vector into two vectors C and R, wherein the C vector takes z (1:1024), the R vector takes z (1024: 2048), and constructing a Hankel matrix with the first column as the C vector, the first row takes z (1:1024), the second row takes z (2:1025), … … and the 1024 th row as the R vector;
carrying out SVD on the constructed Hankel matrix to obtain a characteristic matrix containing the vibration signal of the original rolling bearing, U, S, V;
calling main diagonal elements in the calculated S characteristic matrix, and sequentially making differences on adjacent elements to obtain a characteristic difference value singular value spectrum of the S characteristic value matrix;
according to the maximum characteristic difference value B (max) in the characteristic difference value singular value spectrum, taking 0.5% as the minimum effective correlation characteristic difference value, putting the characteristic difference value of which the characteristic difference value is larger than the threshold value into an S1 matrix, using a zero matrix to complement the dimension of the original S characteristic value matrix, putting the characteristic difference value of which the characteristic difference value is smaller than the threshold value into an S2 matrix, and using the zero matrix to complement the dimension of the original S characteristic value matrix;
and reconstructing a rolling bearing signal data matrix subjected to noise reduction once and a rolling bearing signal data matrix of high-frequency and noise signal components by using H1-U-S1-V, H2-U-S2-V respectively, and taking the first row and the last row of H1 and H2 to complete the SVD noise reduction process of multi-segment data and restore the SVD noise reduction process to a one-dimensional time sequence long-chain data form.
Step three: decomposing and denoising once according to SVD of a long data chain, adaptively determining the added Gaussian white noise standard deviation Nstd and the iterative aggregation times NE of CEEMD by using the extracted noise and high-frequency components, decomposing the denoised signal z1 and performing EMD upper and lower envelopes for multiple times to obtain a plurality of IMF component groups and averaging.
The overall standard deviation parameter from the high frequency and noise component z2 is:
where μ is the overall average of z2, and the overall kurtosis parameter number is, based on the high frequency and noise components z 2:
the standard deviation of the added Gaussian white noise is adaptively determined according to the standard deviation and the kurtosis of different vibration signal data: nstd is omega. std (z2) + nu. ku (z2), wherein the weight omega is 0.2, and the weight nu is 0.05;
and adaptively determining the iterative aggregation times of each CEEMD according to the overall standard deviation parameter quantity of the high-frequency and noise components: NE ═ fix (10 x ln (Nstd +1.5))2Where fix () denotes rounding the value in parentheses to zero and empirically setting a threshold limit NE ∈ [20,100 ]]；
The adaptive number of IMF components is preliminarily set according to general experience as follows: TNM0 ═ fix (log)2(length (z1)) -1), wherein length (z1) is the actual signal length of the noise reduction signal z1 of the rolling bearing;
adding a group of Gaussian white noise signals with different signs and Nstd standard deviation into a rolling bearing noise reduction signal z1, performing EMD decomposition on the Gaussian white noise signals to obtain upper and lower envelope lines and TNM IMF components, and iterating NE times to obtain an average value serving as IMF (1) -IMF (TNM) components;
and intercepting 10 points at the left end and the right end to carry out end point truncation, namely deleting the first 10 sampling point data and the last 10 sampling point data of each IMF component and obtaining each intercepted IMF component, thereby reducing the over-enveloping distortion phenomenon generated at the boundary.
Step four: according to self-adaptive improved CEEMD decomposition, IMF linear correlation is judged and screened, IMF components and residual errors with linear correlation degrees smaller than a certain threshold value are discarded, vibration signals of the rolling bearing are subjected to secondary noise reduction, and a characteristic data chain of the rolling bearing signals subjected to multi-layer noise reduction is reconstructed by summing IMF component groups subjected to secondary noise reduction.
Sequentially verifying and sequentially removing IMF (i) components according to the IMF component group subjected to primary noise reduction, wherein correlation coefficients of rolling bearing residual signals z3 and original signals z1 are subjected to primary noise reduction
Let rhoz1,z30.1 as a threshold for screening linear correlation signals, wherein Cov (z1, z3) represents the covariance of z1 and z3, std (z1), std (z3) represents the variance of z1 and z 3;
according to a correlation coefficient threshold pz1,z3And when the correlation coefficient of z1 and z3 is smaller than a threshold value, removing the IMF components in the residual sequence and the residual error, summing the linearly correlated IMF components, and reconstructing to obtain a rolling bearing signal with secondary noise reduction.
The invention has the beneficial effects that:
the invention carries out noise reduction filtering processing aiming at a large amount of high-frequency signals and background noise signals contained in the original vibration signal data of the rolling bearing, is different from a single noise reduction filtering method, and filters different types of irrelevant signals, residual errors and noises by stages in a multi-layer filtering mode. The method improves the noise reduction type limitation of single filtering, improves the existing SVD singular value decomposition and CEEMD algorithm, reduces the requirement on computer hardware, enhances the self-adaptive improvement capability of CEEMD on different signals, and improves the comprehensive noise reduction capability of the original vibration signals of the rolling bearing.
Drawings
FIG. 1 is a flow chart of the overall steps of a method for multi-layer noise reduction based on improved CEEMD rolling bearing signals;
FIG. 2 is a detailed flowchart of the original rolling bearing signal definition processing steps shown in FIG. 1;
FIG. 3 is a detailed flowchart of the single denoising step of SVD shown in FIG. 1;
FIG. 4 is a detailed flow chart of the adaptive modified CEEMD decomposition step shown in FIG. 1;
FIG. 5 is a detailed flow chart of the IMF component linear independent component quadratic noise reduction step shown in FIG. 1;
Detailed Description
The technical route and method of the present invention will be described in more detail with reference to the accompanying drawings, but it should not be construed that the technical scheme of the present invention is limited, and modifications and substitutions of the method, steps or conditions of the present invention are included within the scope of the present invention without departing from the essence of the present invention.
In order to improve the comprehensive noise reduction capability of the original vibration signal of the rolling bearing, the embodiment discloses a multilayer noise reduction method based on an improved CEEMD rolling bearing signal, which specifically comprises four main steps:
firstly, time definition processing is carried out on an original rolling bearing signal, the length of the rolling bearing signal to be analyzed is intercepted, a corresponding time point of each signal sampling point is defined, and the corresponding matching with actual sampling time, sampling duration and sampling frequency is carried out.
According to the length of a data chain for sampling the vibration signal of the rolling bearing, the data chain is adaptively decomposed into a plurality of column vectors of 2048 × 1, and the total decomposition number is as follows:
in the formula, fix () is rounding to zero in parentheses, and the iteration counter is: m is 1,2, …, M;
defining the amount of time for each data point as
In which i e [1,2048 ]]。
Step two: and according to the definition processing of the original rolling bearing signal, performing disassembly and assembly iteration operation on the long data chain of the rolling bearing vibration signal, disassembling the original data chain into a plurality of short data chains, and performing SVD decomposition and noise reduction.
Splitting each 2048 x 1 column vector into two vectors C and R, wherein z (1:1024) is taken from the C vector, z (1024: 2048) is taken from the R vector, a first column is constructed as the C vector, z (1:1024) is taken from the first row, z (2:1025) is taken from the second row, and the 1024 th row is a Hankel matrix of the R vector;
carrying out SVD on the constructed Hankel matrix to obtain a characteristic matrix containing the vibration signal of the original rolling bearing, U, S, V;
calling main diagonal elements in the calculated S characteristic matrix, and sequentially making differences on adjacent elements to obtain a characteristic difference value singular value spectrum of the S characteristic value matrix;
according to the maximum characteristic difference value B (max) in the characteristic difference value singular value spectrum, taking 0.5% as the minimum effective correlation characteristic difference value, putting the characteristic difference value of which the characteristic difference value is larger than the threshold value into an S1 matrix, using a zero matrix to complement the dimension of the original S characteristic value matrix, putting the characteristic difference value of which the characteristic difference value is smaller than the threshold value into an S2 matrix, and using the zero matrix to complement the dimension of the original S characteristic value matrix;
and reconstructing a rolling bearing signal data matrix subjected to primary noise reduction and a rolling bearing signal data matrix of high-frequency and noise signal components by using H1-U-S1-V, H2-U-S2-V respectively, and taking the first row and the last row of H1 and H2 to finish the SVD noise reduction process of multi-segment data and restore the SVD noise reduction process into a one-dimensional time sequence long-chain data form.
Step three: decomposing and denoising once according to SVD of a long data chain, adaptively determining the added Gaussian white noise standard deviation Nstd and the iterative aggregation times NE of CEEMD by using the extracted noise and high-frequency components, decomposing the denoised signal z1 and performing EMD upper and lower envelopes for multiple times to obtain a plurality of IMF component groups and averaging.
General scaling from high frequency and noise components z2The tolerance parameter quantity is:
where μ is the overall average of z2, and the overall kurtosis parameter number is, based on the high frequency and noise components z 2:
the standard deviation of the added Gaussian white noise is adaptively determined according to the standard deviation and the kurtosis of different vibration signal data: nstd is omega. std (z2) + nu. ku (z2), wherein the weight omega is 0.2, and the weight nu is 0.05;
and adaptively determining the iterative aggregation times of each CEEMD according to the overall standard deviation parameter quantity of the high-frequency and noise components: NE ═ fix (10 x ln (Nstd +1.5))2Where fix () denotes rounding the value in parentheses to zero and empirically setting a threshold limit NE ∈ [20,100 ]]The method can prevent the over-large NE, which causes the over-large calculation amount, and the over-small NE, which causes the failure of the method;
the adaptive number of IMF components is preliminarily set according to general experience as follows: TNM0 ═ fix (log)2(length (z1)) -1), wherein length (z1) is the actual signal length of the noise reduction signal z1 of the rolling bearing;
adding a group of rolling bearing noise reduction signals z1 into a group of white Gaussian noise signals with different signs and standard deviation of Nstd, performing EMD decomposition on the white Gaussian noise signals to obtain upper and lower envelope lines and TNM IMF components, and iterating NE times to obtain an average value serving as IMF (1) to IMF (TNM) components;
and intercepting 10 points at the left end and the right end to carry out end point truncation, namely deleting the first 10 sampling point data and the last 10 sampling point data of each IMF component and obtaining each intercepted IMF component, thereby reducing the over-enveloping distortion phenomenon generated at the boundary.
Step four: according to self-adaptive improved CEEMD decomposition, IMF linear correlation is judged and screened, IMF components and residual errors with linear correlation degrees smaller than a certain threshold value are discarded, vibration signals of the rolling bearing are subjected to secondary noise reduction, and a characteristic data chain of the rolling bearing signals subjected to multi-layer noise reduction is reconstructed by summing IMF component groups subjected to secondary noise reduction.
Sequentially verifying and sequentially removing IMF (i) components according to the IMF component group subjected to primary noise reduction, wherein correlation coefficients of rolling bearing residual signals z3 and original signals z1 are subjected to primary noise reduction
Let rhoz1,z30.1 as a threshold for screening linear correlation signals, wherein Cov (z1, z3) represents the covariance of z1 and z3, std (z1), std (z3) represents the variance of z1 and z 3;
according to a correlation coefficient threshold pz1,z3And when the correlation coefficient of z1 and z3 is smaller than a threshold value, removing the IMF components in the residual sequence and the residual error, summing the linearly correlated IMF components, and reconstructing to obtain a rolling bearing signal with secondary noise reduction.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. However, the above description is only an example of the present invention, the technical features of the present invention are not limited thereto, and any other embodiments that can be obtained by those skilled in the art without departing from the technical solution of the present invention should be covered by the claims of the present invention.
Claims (5)
1. A multilayer noise reduction method based on improved CEEMD rolling bearing signals is characterized by comprising the following steps:
firstly, time definition processing is carried out on an original rolling bearing signal, the length of the rolling bearing signal to be analyzed is intercepted, a corresponding time point of each signal sampling point is defined, and the corresponding matching with actual sampling time, sampling duration and sampling frequency is carried out;
step two, defining and processing according to an original rolling bearing signal, performing disassembly and assembly iteration operation on a long data chain of a rolling bearing vibration signal, disassembling the original data chain into a plurality of short data chains, and performing SVD decomposition and noise reduction;
decomposing and denoising once according to the SVD of the long data chain, determining the added Gaussian white noise standard deviation Nstd and the iterative polymerization times NE of the CEEMD in a self-adaptive manner by utilizing the extracted noise and high-frequency components, decomposing and enveloping the denoised signal z1 for multiple times of EMD, and obtaining and averaging multiple IMF component groups;
and step four, judging and screening IMF linear correlation according to self-adaptive improved CEEMD decomposition, giving up IMF components and residual errors with the linear correlation degree smaller than a threshold value, secondarily reducing the vibration signals of the rolling bearing, and summing IMF component groups subjected to secondary noise reduction to reconstruct a characteristic data chain of the rolling bearing signals subjected to multilayer noise reduction.
2. The method for multi-layer noise reduction based on improved CEEMD rolling bearing signals according to claim 1, wherein the step 1 is implemented by:
(1) according to the length of a data chain for sampling the vibration signal of the rolling bearing, the data chain is adaptively decomposed into a plurality of column vectors of 2048 × 1, and the total decomposition number is as follows:
in the formula, fix () is rounding to zero in parentheses, and the iteration counter is: m is 1,2, …, M;
(2) defining the amount of time for each data point as
In which i e [1,2048 ]]。
3. The method for multi-layer noise reduction based on improved CEEMD rolling bearing signals according to claim 2, wherein the step 2 is implemented by:
(1) splitting each 2048 x 1 column vector into two vectors C and R, wherein the C vector takes z (1:1024), the R vector takes z (1024: 2048), and constructing a Hankel matrix with the first column as the C vector, the first row takes z (1:1024), the second row takes z (2:1025), … … and the 1024 th row as the R vector;
(2) carrying out SVD on the constructed Hankel matrix to obtain a characteristic matrix containing the vibration signal of the original rolling bearing, U, S, V;
(3) calling main diagonal elements in the calculated S characteristic matrix, and sequentially making differences on adjacent elements to obtain a characteristic difference value singular value spectrum of the S characteristic value matrix;
(4) according to the maximum characteristic difference value B (max) in the characteristic difference value singular value spectrum, taking 0.5% as the minimum effective correlation characteristic difference value, putting the characteristic difference value of which the characteristic difference value is larger than the threshold value into an S1 matrix, using a zero matrix to complement the dimension of the original S characteristic value matrix, putting the characteristic difference value of which the characteristic difference value is smaller than the threshold value into an S2 matrix, and using the zero matrix to complement the dimension of the original S characteristic value matrix;
(5) and reconstructing a rolling bearing signal data matrix subjected to primary noise reduction and a rolling bearing signal data matrix of high-frequency and noise signal components by using H1-U-S1-V, H2-U-S2-V respectively, and taking the first row and the last row of H1 and H2 to finish the SVD noise reduction process of multi-segment data and restore the SVD noise reduction process into a one-dimensional time sequence long-chain data form.
4. The method for multi-layer noise reduction based on improved CEEMD rolling bearing signals according to claim 3, wherein the specific implementation process of the third step is as follows:
(1) the overall standard deviation parameter from the high frequency and noise component z2 is:
where μ is the overall average of z2, and the overall kurtosis parameter number is, based on the high frequency and noise components z 2:
(2) the standard deviation of the added Gaussian white noise is adaptively determined according to the standard deviation and the kurtosis of different vibration signal data: nstd is omega. std (z2) + nu. ku (z2), wherein the weight omega is 0.2, and the weight nu is 0.05;
(3) and adaptively determining the iterative aggregation times of each CEEMD according to the overall standard deviation parameter quantity of the high-frequency and noise components: NE ═ fix (10 x ln (Nstd +1.5))2Where fix () denotes rounding the value in parentheses to zero and empirically setting a threshold limit NE ∈ [20,100 ]]；
(4) According to the generalThe adaptive number of IMF components is preliminarily set by experience as: TNM0 ═ fix (log)2(length (z1)) -1), wherein length (z1) is the actual signal length of the noise reduction signal z1 of the rolling bearing;
(5) adding a group of Gaussian white noise signals with different signs and Nstd standard deviation into a rolling bearing noise reduction signal z1, performing EMD decomposition on the Gaussian white noise signals to obtain upper and lower envelope lines and TNM IMF components, and iterating NE times to obtain an average value serving as IMF (1) -IMF (TNM) components;
(6) and intercepting 10 points at the left end and the right end to carry out end point truncation, namely deleting the first 10 sampling point data and the last 10 sampling point data of each IMF component and obtaining each intercepted IMF component, thereby reducing the over-enveloping distortion phenomenon generated at the boundary.
5. The method for multi-layer noise reduction based on improved CEEMD rolling bearing signals according to claim 4, wherein the step four is implemented by the following steps:
(1) sequentially verifying and sequentially removing IMF (i) components according to the IMF component group subjected to primary noise reduction, wherein correlation coefficients of rolling bearing residual signals z3 and original signals z1 are subjected to primary noise reduction
Let rhoz1,z30.1 as a threshold for screening linear correlation signals, wherein Cov (z1, z3) represents the covariance of z1 and z3, std (z1), std (z3) represents the variance of z1 and z 3;
(2) according to a linear correlation threshold pz1,z3And when the correlation coefficient of z1 and z3 is smaller than a threshold value, removing the IMF components in the residual sequence and the residual error, summing the linearly correlated IMF components, and reconstructing a rolling bearing signal subjected to secondary noise reduction.
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Cited By (8)
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| CN113375940A (en) * | 2021-05-28 | 2021-09-10 | 三峡大学 | Fault bearing diagnosis method based on SVD and CEEMDAN |
| CN113625125A (en) * | 2021-09-24 | 2021-11-09 | 南方电网科学研究院有限责任公司 | High-resistance ground fault detection method, device and equipment for power distribution network |
| CN113625125B (en) * | 2021-09-24 | 2023-11-21 | 南方电网科学研究院有限责任公司 | High-resistance ground fault detection method, device and equipment for power distribution network |
| CN114264478A (en) * | 2021-12-21 | 2022-04-01 | 北京石油化工学院 | Diesel engine crankshaft bearing wear degree prediction method and system |
| CN117194901A (en) * | 2023-11-07 | 2023-12-08 | 上海伯镭智能科技有限公司 | Unmanned vehicle working state monitoring method and system |
| CN117194901B (en) * | 2023-11-07 | 2024-02-02 | 上海伯镭智能科技有限公司 | Unmanned vehicle working state monitoring method and system |
| CN117349661A (en) * | 2023-12-04 | 2024-01-05 | 浙江大学高端装备研究院 | Method, device, equipment and storage medium for extracting vibration signal characteristics of plunger pump |
| CN117349661B (en) * | 2023-12-04 | 2024-02-20 | 浙江大学高端装备研究院 | Method, device, equipment and storage medium for extracting vibration signal characteristics of plunger pump |
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