CN114441174B - Method, system, equipment and medium for diagnosing composite fault of rolling bearing - Google Patents
Method, system, equipment and medium for diagnosing composite fault of rolling bearing Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a medium for diagnosing a composite fault of a rolling bearing, wherein the method comprises the following steps: acquiring a vibration signal of a rolling bearing; decomposing the vibration signal to obtain an identity orthogonal matrix; denoising and evaluating the unit orthogonal matrix to obtain a filtered signal; performing envelope analysis processing on the filtered signal to obtain an envelope spectrum; and diagnosing the composite fault of the rolling bearing according to the envelope spectrum. The method comprises the steps of decomposing an acquired vibration signal of a rolling bearing to obtain a unit orthogonal matrix; denoising and evaluating the unit orthogonal matrix to obtain a filtered signal; and then diagnosing the composite fault of the rolling bearing according to an envelope spectrum obtained after envelope analysis processing is carried out on the filtering signal, thereby enhancing the weak fault characteristics in the composite fault characteristics and realizing the self-adaptive extraction of the composite fault characteristics of the rolling bearing.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of mechanical equipment, in particular to a method, a system, equipment and a medium for diagnosing a composite fault of a rolling bearing.
Background
The rolling bearing is an extremely critical bearing component in various rotary machines, such as a turbine engine, a centrifugal machine, a wind driven generator, an automobile gearbox and the like, and once the rolling bearing fails, the rolling bearing can cause serious consequences such as abnormal shutdown and the like of mechanical equipment, so that the fault identification and diagnosis of the rolling bearing have important significance for guaranteeing the safe and reliable operation of the mechanical equipment. The research on single faults of the bearing is very mature, however, in the actual running process of equipment, a plurality of faults often exist in the fault bearing. The composite fault signals are mutually offset and overlapped in the time domain, and the complex transmission path is influenced, so that the extraction difficulty of the composite fault characteristics is further increased.
In the prior art, although some feature extraction methods of composite faults are also proposed, most of the fault features cannot be adaptively extracted, and multiple fault features can be completely extracted only by inputting additional priori knowledge and repeating operation for multiple times. Thus, adaptive extraction of composite fault signatures remains an urgent problem to be solved.
Disclosure of Invention
The invention aims to overcome the defect that the fault extraction method adopted in the prior art cannot realize self-adaptive extraction of composite fault characteristics, and provides a method, a system, equipment and a medium for diagnosing composite faults of a rolling bearing.
The invention solves the technical problems by the following technical scheme:
The first aspect of the invention provides a method for diagnosing a composite fault of a rolling bearing, comprising the steps of:
acquiring a vibration signal of a rolling bearing;
Decomposing the vibration signal to obtain an identity orthogonal matrix;
Denoising and evaluating the unit orthogonal matrix to obtain a filtered signal;
Performing envelope analysis processing on the filtered signal to obtain an envelope spectrum;
And diagnosing the composite fault of the rolling bearing according to the envelope spectrum.
Preferably, the step of decomposing the vibration signal to obtain a unit orthogonal matrix includes:
Cutting off and removing the mean value of the vibration signal to obtain a vibration signal after cutting off and removing the mean value;
generating a toeplitz matrix by using the vibration signals subjected to the truncation and mean removal treatment;
and decomposing the Toeplitz matrix by adopting singular values to obtain the unit orthogonal matrix.
Preferably, the step of denoising and evaluating the unit orthogonal matrix to obtain a filtered signal includes:
Carrying out denoising treatment on the unit orthogonal matrix by adopting sparse coding contraction so as to obtain a denoised unit orthogonal matrix;
calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising treatment;
Calculating according to the comparison result of the cyclic characteristic information ratio and a preset cyclic characteristic information ratio threshold value to obtain an iteration result of the unit orthogonal matrix after the denoising processing;
And calculating the filtering signal according to the iteration result.
Preferably, the step of diagnosing the rolling bearing composite fault according to the envelope spectrum includes:
Extracting the characteristic frequency of the rolling bearing composite fault according to the envelope spectrum;
and diagnosing the composite fault of the rolling bearing according to the characteristic frequency.
The invention provides a diagnosis system for a composite fault of a rolling bearing, which comprises an acquisition module, a decomposition module, a first processing module, a second processing module and a diagnosis module;
the acquisition module is used for acquiring vibration signals of the rolling bearing;
The decomposition module is used for decomposing the vibration signal to obtain a unit orthogonal matrix;
The first processing module is used for carrying out denoising and evaluation processing on the unit orthogonal matrix so as to obtain a filtered signal;
The second processing module is used for carrying out envelope analysis processing on the filtered signal so as to obtain an envelope spectrum;
and the diagnosis module is used for diagnosing the rolling bearing composite fault according to the envelope spectrum.
Preferably, the decomposition module comprises a first processing unit, a generating unit and a decomposition unit;
the first processing unit is used for carrying out truncation and mean value removal processing on the vibration signals so as to obtain truncated and mean value removed vibration signals;
The generating unit is used for generating a toeplitz matrix by using the vibration signals subjected to the truncation and mean removal processing;
The decomposition unit is used for decomposing the Toeplitz matrix by adopting singular values so as to obtain the unit orthogonal matrix.
Preferably, the first processing module comprises a second processing unit, a first computing unit, a second computing unit and a third computing unit;
The second processing unit is used for carrying out denoising processing on the unit orthogonal matrix by adopting sparse coding shrinkage so as to obtain a denoised unit orthogonal matrix;
The first calculation unit is used for calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising treatment;
the second calculation unit is used for calculating and obtaining an iteration result of the unit orthogonal matrix after the denoising process according to a comparison result of the cyclic characteristic information ratio and a preset cyclic characteristic information ratio threshold;
the third calculation unit is configured to calculate the filtered signal according to the iteration result.
Preferably, the diagnosis module comprises an extraction unit and a diagnosis unit;
The extraction unit is used for extracting the characteristic frequency of the rolling bearing composite fault according to the envelope spectrum;
And the diagnosis unit is used for diagnosing the composite fault of the rolling bearing according to the characteristic frequency.
A third aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of diagnosing a composite fault of a rolling bearing according to the first aspect when executing the computer program.
A fourth aspect of the invention provides a computer-readable storage medium on which a computer program is stored which, when executed by a processor, implements a method of diagnosing a composite fault of a rolling bearing according to the first aspect.
On the basis of conforming to the common knowledge in the field, the preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the invention.
The invention has the positive progress effects that:
The method comprises the steps of decomposing an acquired vibration signal of a rolling bearing to obtain a unit orthogonal matrix; denoising and evaluating the unit orthogonal matrix to obtain a filtered signal; and then diagnosing the composite fault of the rolling bearing according to an envelope spectrum obtained after envelope analysis processing is carried out on the filtering signal, thereby enhancing the weak fault characteristics in the composite fault characteristics and realizing the self-adaptive extraction of the composite fault characteristics of the rolling bearing.
Drawings
Fig. 1 is a flowchart of a method for diagnosing a composite failure of a rolling bearing according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of the step 102 of the method for diagnosing a composite failure of a rolling bearing according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of step 103 of the method for diagnosing a composite failure of a rolling bearing according to embodiment 1 of the present invention.
Fig. 4 is a flowchart of step 105 of the method for diagnosing a composite failure of a rolling bearing according to embodiment 1 of the present invention.
Fig. 5 is a schematic diagram of the structure of the test stand according to the embodiments 1 and 2 of the present invention.
Fig. 6 is a schematic diagram of the processed vibration signal x (n) of embodiments 1 and 2 of the present invention.
Fig. 7 is a schematic diagram of envelope spectra of vibration signals x (n) of embodiments 1 and 2 of the present invention.
Fig. 8 is a schematic diagram showing the cyclic characteristic information ratio of each unit orthogonal matrix of embodiments 1 and 2 of the present invention.
Fig. 9 is a schematic diagram of the deconvolution filtering result of the maximum correlation kurtosis when the input period of embodiments 1 and 2 is the inner ring failure characteristic frequency.
Fig. 10 is a schematic diagram of the envelope spectra of the corresponding vibration signals of embodiments 1 and 2 of the present invention.
Fig. 11 is a schematic diagram of the deconvolution filtering result of the maximum correlation kurtosis when the input period of embodiments 1 and 2 of the present invention is the outer ring fault characteristic frequency.
Fig. 12 is a schematic diagram of the envelope spectra of the corresponding vibration signals of embodiments 1 and 2 of the present invention.
Fig. 13 is a schematic diagram of vibration signals after multi-period blind deconvolution processing according to embodiments 1 and 2 of the present invention.
Fig. 14 is a schematic diagram of the envelope spectrum of the corresponding vibration signal after the multi-period blind deconvolution process according to embodiments 1 and 2 of the present invention.
Fig. 15 is a block diagram of a diagnosis system of a composite failure of a rolling bearing according to embodiment 2 of the present invention.
Fig. 16 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
The present embodiment provides a method for diagnosing a composite fault of a rolling bearing, as shown in fig. 1, the method comprising:
Step 101, obtaining a vibration signal of a rolling bearing;
in this embodiment, the vibration acceleration sensor is attached to the bearing seat of the rolling bearing to be measured, and the vibration signal of the rolling bearing is collected by the vibration acceleration sensor.
102, Decomposing the vibration signal to obtain an identity orthogonal matrix;
Step 103, denoising and evaluating the unit orthogonal matrix to obtain a filtered signal;
In this embodiment, the phase space formed by the collected vibration signals is decomposed by SVD (singular value decomposition), and the signal components are separated to screen out an effective unit orthogonal matrix, specifically, the noise of the unit orthogonal matrix is suppressed by performing denoising processing on the unit orthogonal matrix, and then the weak fault characteristics in the multiple fault characteristics are enhanced by evaluating the fault information amounts of the unit orthogonal matrices of each pair and retaining the effective unit orthogonal matrix (i.e. obtaining the filtered signal), so as to enhance the weak fault characteristics with abundant information.
104, Carrying out envelope analysis processing on the filtered signal to obtain an envelope spectrum;
and 105, diagnosing the composite fault of the rolling bearing according to the envelope spectrum.
In one embodiment, as shown in FIG. 2, step 102 comprises:
Step 1021, performing truncation and mean value removal processing on the vibration signal to obtain a vibration signal subjected to truncation and mean value removal processing;
In this embodiment, the vibration signal collected by the vibration acceleration sensor is subjected to truncation and averaging processing, so as to obtain a vibration signal after the truncation and averaging processing, i.e., a vibration signal x (n) of a processed time sequence.
Step 1022, generating a toeplitz matrix by using the vibration signals subjected to the truncation and mean removal treatment;
In this embodiment, the iteration number k and the filter length L are preset, and then the toeplitz matrix is generated by using the vibration signal x (n) according to the formula (1):
X 0 denotes the toeplitz matrix, X N denotes the last element of the time series of vibration signals, and N denotes the length of the vibration signals.
Step 1023, decomposing the Toeplitz matrix by adopting singular values to obtain the unit orthogonal matrix.
In this embodiment, singular value decomposition is used on the toeplitz matrix X 0 obtained in step 1022 to obtain the unit orthogonal matrix U, V and the diagonal matrix Σ, and the matrix is divided into two parts according to the filter length L as shown in formula (2):
wherein,
U＝[u1 u2 ... uL]∈RL×L (3)
∑＝[diag(σ1,σ2,...,σL),0]∈RL×(N-L+1) (4)
V＝[v1 v2 ... vN-L+1]∈R(N-L+1)×(N-L+1) (5)
Wherein U 1、V1 and Σ 1 represent a singular value unity orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace, U 2、V2 and Σ 2 represent a singular value unity orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace, R represents a real set, σ 1＞σ2＞...＞σL.
Substituting formula (2) into a deconvolution iterative formula, namely formula (6), to obtain formula (7):
where y represents the deconvolution iteration result, V L＝[v1 v2 ... vL.
In one embodiment, as shown in FIG. 3, step 103 includes:
Step 1031, performing denoising treatment on the unit orthogonal matrix by adopting sparse coding shrinkage to obtain a denoised unit orthogonal matrix;
In this embodiment, to reduce the influence of noise on the result, sparse coding contraction is performed on each column x [ n ] in the V L identity orthogonal matrix according to equation (8):
wherein, Each column vector in the V L unit orthogonal matrix after sparse coding shrinkage is represented, r represents a parameter for adjusting the noise reduction effect, and the greater r is, the more sparse the noise reduction result is; Sigma represents the standard deviation of noise, which is similar to the average absolute error of the unit orthogonal matrix; σ x represents the standard deviation of the identity orthogonal matrix. When the square root in the formula (8) is an imaginary number, Set to 0.
Normalizing the unit orthogonal matrix after sparse coding shrinkage according to a formula (9):
wherein, Each column vector in the normalized V L unit orthogonal matrix is represented.
Step 1032, calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising process;
in this embodiment, in order to evaluate the information amount of each unit orthogonal matrix, the cyclic characteristic information ratio of each unit orthogonal matrix is used to evaluate the information amount of the unit orthogonal matrix in the specific implementation process.
Firstly, performing Hilbert on each unit orthogonal matrix according to a formula (10) and a formula (11) to obtain an analysis signal of the unit orthogonal matrix, and then calculating to obtain a square envelope spectrum:
the above is a specific calculation of the square envelope spectrum, wherein, Representing the result of the envelope processing, SES k represents the squared envelope spectrum.
Secondly, calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising processing according to the formula (12):
Wherein SES [ K ] max represents the largest value in SES [ K ], SES S (round (sK)) represents the round (sK) number in SES S after SES [ K ] is arranged from small to large, s represents a real number smaller than 1, K represents the length of the square envelope spectrum, namely K= (N-L+1)/2, K represents the kth point in the square envelope spectrum, and round (sK) represents rounding operation.
Step 1033, calculating to obtain an iteration result of the unit orthogonal matrix after denoising according to a comparison result of the cycle characteristic information ratio and a preset cycle characteristic information ratio threshold;
in this embodiment, a loop feature information ratio threshold p is preset, and the iteration result of each denoised unit orthogonal matrix is calculated based on the comparison result of the loop feature information ratio and the preset loop feature information ratio threshold according to formula (13):
Wherein V 1 represents the 1 st unit orthogonal matrix in V L unit orthogonal matrices, CCIR 1 represents the cyclic characteristic information ratio of the 1 st unit orthogonal matrix in V L unit orthogonal matrices, and x 1 represents the iteration result of the 1 st unit orthogonal matrix in V L unit orthogonal matrices.
Step 1034, calculating to obtain a filtered signal according to the iteration result.
In this embodiment, the final filtered signal is obtained according to equation (14):
The above is a calculation process of the filtered signal, and x re represents the finally obtained filtered signal.
In one embodiment, as shown in FIG. 4, step 105 includes:
1051, extracting the characteristic frequency of the rolling bearing composite fault according to an envelope spectrum;
And step 1052, diagnosing the composite fault of the rolling bearing according to the characteristic frequency.
In this embodiment, envelope analysis is performed on the filtered signal x re, and an envelope spectrum is obtained, so that the characteristic frequency of the rolling bearing composite fault is extracted, and finally the rolling bearing composite fault is identified according to the characteristic frequency.
In the specific implementation process, under the condition that the extracted characteristic frequency of the composite fault of the rolling bearing is consistent with the fault frequency of the rolling bearing, the composite fault of the rolling bearing is identified, the self-adaptive extraction of the composite fault characteristic of the rolling bearing is realized, and the diagnosis of the composite fault of the rolling bearing based on multi-period blind deconvolution is further realized.
The embodiment is derived from the traditional minimum entropy deconvolution, but breaks through the limitation of optimizing an objective function of a deconvolution method, decomposes a phase space formed by collected vibration signals through SVD, screens an effective unit orthogonal matrix, and finally runs to realize the feature extraction of the composite fault; the information quantity of the singular value unit orthogonal matrix is used as an index to establish a screening criterion, and a reconstruction component signal is not required to be screened, so that the influence of energy difference on the information quantity is eliminated; the method has the advantages that extra fault characteristic periods are not required to be provided, the self-adaptive extraction of the composite fault characteristics of the rolling bearing is realized according to weak fault characteristics with circulating characteristic information richer than reinforcing information, and the diagnosis of the composite fault of the rolling bearing based on multi-period blind deconvolution is further realized.
The following description is made in connection with specific examples:
For example, as shown in fig. 5, a certain locomotive bearing test bed is taken as an example, and the test bed is composed of a hydraulic motor, a driving wheel, a bearing, a locomotive wheel and the like, wherein the hydraulic motor drives the driving wheel, the driving wheel is contacted with an outer ring of the bearing and drives the outer ring of the bearing to move, an inner ring of the bearing is fixed on an axle of the locomotive wheel pair, and an acceleration sensor is fixed at one end of the bearing and is used for measuring vibration signals of the bearing.
In a specific implementation process, a vibration signal of a certain locomotive axle test bed is collected through an acceleration sensor, and the collected vibration signal is subjected to truncation and mean value removal processing to obtain a vibration signal x (N) subjected to truncation and mean value removal processing, for example, the sampling frequency is set to be 76.8kHz, the sampling duration is set to be 0.5s, the length N of the vibration signal is calculated to be 38400, the processed vibration signal x (N) is shown in fig. 6, and the envelope spectrum of the vibration signal x (N) is shown in fig. 7;
the iteration number k=100 and the filter length l=30 are preset, and then the toeplitz matrix X 0 is generated by using the vibration signal X (n) according to the formula (1):
Singular value decomposition is used on the generated toeplitz matrix X 0 to obtain a unit orthogonal matrix U, V and a diagonal matrix Σ, and the matrix is divided into two parts according to the filter length l=30 as shown in formula (2):
wherein,
U＝[u1 u2 ... u30]∈R30×30 (3)
∑＝[diag(σ1,σ2,...,σ30),0]∈R30×38370) (4)
V＝[v1 v2 ... v38370]∈R38370×38370) (5)
Wherein U 30、V30 and Σ 30 represent a singular value unit orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace formed by the first 30 items of each matrix, U 38370、V38370 and Σ 38370 represent a singular value unit orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace remaining in each matrix, and R represents a real number set and σ 1＞σ2＞...＞σ30.
Substituting formula (2) into a deconvolution iterative formula, namely formula (6), to obtain formula (7):
where y represents the deconvolution iteration result, V 30＝[v1 v2 ... v30.
To reduce the effect of noise on the result, each column x [ n ] in the V 30 unit orthogonal matrix is sparse coded and shrunk according to equation (8):
wherein, Each column vector in the V 30 unit orthogonal matrix after sparse coding shrinkage is represented, r represents a parameter for adjusting the noise reduction effect, and the greater r is, the more sparse the noise reduction result is, and in this embodiment r is set to 3; Sigma represents the standard deviation of noise, which is similar to the average absolute error of the unit orthogonal matrix; σ x represents the standard deviation of the identity orthogonal matrix. When the square root in the formula (8) is an imaginary number, Set to 0.
Normalizing the unit orthogonal matrix after sparse coding shrinkage according to a formula (9):
wherein, Each column vector in the normalized V 30 unit orthogonal matrix is represented.
In order to evaluate the information amount of each unit orthogonal matrix, the cyclic characteristic information ratio of each unit orthogonal matrix is used to evaluate the information amount of the unit orthogonal matrix.
Firstly, performing Hilbert on each unit orthogonal matrix according to a formula (10) and a formula (11) to obtain an analysis signal of the unit orthogonal matrix, and then calculating to obtain a square envelope spectrum:
the above is a specific calculation of the square envelope spectrum, wherein, Representing the result of the envelope processing, SES is the squared envelope spectrum.
Secondly, calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising processing according to the formula (12):
Wherein SES [ K ] max represents the largest value in SES [ K ], SES S (round (sK)) represents the round (sK) number in SES S after the SES [ K ] are arranged from small to large, s represents a real number smaller than 1, K represents the length of the square envelope spectrum, i.e., k=19185, K represents the kth point in the square envelope spectrum, and round (sK) represents performing rounding operation. The cyclic characteristic information of each unit orthogonal matrix of V 30 is shown in fig. 8.
A loop characteristic information ratio threshold p=4 is preset, and the iteration result of each denoising unit orthogonal matrix is calculated according to the comparison result of the loop characteristic information ratio and the preset loop characteristic information ratio threshold based on a formula (13):
Wherein V 1 represents the 1 st unit orthogonal matrix in V 30 unit orthogonal matrices, CCIR 1 represents the cyclic characteristic information ratio of the 1 st unit orthogonal matrix in V 30 unit orthogonal matrices, and x 1 represents the iteration result of the 1 st unit orthogonal matrix in V 30 unit orthogonal matrices.
Obtaining a final filtered signal according to equation (14):
The above is a calculation process of the filtered signal, and x re represents the finally obtained filtered signal.
In this embodiment, envelope analysis is performed on the filtered signal x re, and an envelope spectrum is obtained, so as to extract the fault characteristic frequency of the rolling bearing, and finally, the composite fault of the rolling bearing is identified according to the characteristic frequency.
In this embodiment, fig. 9 is a result of deconvolution filtering of the maximum correlation kurtosis when the input period is the inner ring fault feature frequency, fig. 10 is a corresponding vibration signal envelope spectrum, fig. 11 is a result of deconvolution filtering of the maximum correlation kurtosis when the input period is the outer ring fault feature frequency, and fig. 12 is a corresponding vibration signal envelope spectrum. As can be seen from fig. 9, 10, 11, and 12, the fault impact signal cannot be extracted effectively when the input period is the outer ring characteristic frequency, and the fault characteristic frequency is difficult to find in the envelope spectrum, so that a plurality of fault characteristic signals cannot be extracted adaptively. Fig. 13 is a graph of vibration signals after multi-period blind deconvolution, and fig. 14 is a graph of envelope spectra of corresponding vibration signals after multi-period blind deconvolution. As can be seen from fig. 13 and 14, the extraction method adopted in the present application significantly and completely extracts a plurality of fault vibration signals, and significantly discovers the fault characteristic frequencies of the inner ring and the outer ring in the envelope spectrum, compared with the conventional methods of fig. 9, 10, 11 and 12. The method effectively verifies that the self-adaptive extraction of the composite fault characteristics of the rolling bearing can be realized without priori knowledge.
In the embodiment, the acquired vibration signal of the rolling bearing is decomposed to obtain a unit orthogonal matrix; denoising and evaluating the unit orthogonal matrix to obtain a filtered signal; and then diagnosing the composite fault of the rolling bearing according to an envelope spectrum obtained after envelope analysis processing is carried out on the filtering signal, thereby enhancing the weak fault characteristics in the composite fault characteristics, realizing self-adaptive extraction of the composite fault characteristics of the rolling bearing, and further realizing diagnosis of the composite fault of the rolling bearing based on multi-period blind deconvolution.
Example 2
The present embodiment provides a diagnosis system of a rolling bearing composite fault, as shown in fig. 15, which includes an acquisition module 1, a decomposition module 2, a first processing module 3, a second processing module 4, and a diagnosis module 5;
the acquisition module 1 is used for acquiring vibration signals of the rolling bearing;
in this embodiment, the vibration acceleration sensor is attached to the bearing seat of the rolling bearing to be measured, and the vibration signal of the rolling bearing is collected by the vibration acceleration sensor.
The decomposition module 2 is used for decomposing the vibration signal to obtain a unit orthogonal matrix;
a first processing module 3, configured to perform denoising and evaluation processing on the unit orthogonal matrix to obtain a filtered signal;
In this embodiment, the phase space formed by the collected vibration signals is decomposed by SVD, and the signal components are separated to screen out an effective unit orthogonal matrix, specifically, the noise of the unit orthogonal matrix is suppressed by denoising the unit orthogonal matrix, and then the weak fault characteristics of the information-rich weak fault characteristics are enhanced by evaluating the fault information quantity of each pair of unit orthogonal matrices and retaining the effective unit orthogonal matrix (i.e. obtaining the filtering signal), thereby realizing the enhancement of the weak fault characteristics in the multi-fault characteristics.
The second processing module 4 is used for carrying out envelope analysis processing on the filtered signal so as to obtain an envelope spectrum;
and the diagnosis module 5 is used for diagnosing the composite fault of the rolling bearing according to the envelope spectrum.
In an embodiment, as shown in fig. 15, the decomposition module 2 includes a first processing unit 21, a generating unit 22, and a decomposition unit 23;
A first processing unit 21, configured to perform truncation and averaging processing on the vibration signal, so as to obtain a vibration signal after the truncation and averaging processing;
In this embodiment, the vibration signal collected by the vibration acceleration sensor is subjected to truncation and averaging processing, so as to obtain a vibration signal after the truncation and averaging processing, i.e., a vibration signal x (n) of a processed time sequence.
A generating unit 22 for generating a toeplitz matrix using the truncated and de-averaged vibration signals;
In this embodiment, the iteration number k and the filter length L are preset, and then the toeplitz matrix is generated by using the vibration signal x (n) according to the formula (1):
X 0 denotes the toeplitz matrix, X N denotes the last element of the time series of vibration signals, and N denotes the length of the vibration signals.
And a decomposition unit 23 for decomposing the toeplitz matrix by using the singular values to obtain a unit orthogonal matrix.
In this embodiment, singular value decomposition is used on the toeplitz matrix X 0 obtained in step 1022 to obtain the unit orthogonal matrix U, V and the diagonal matrix Σ, and the matrix is divided into two parts according to the filter length L as shown in formula (2):
wherein,
U＝[u1 u2 ... uL]∈RL×L (3)
∑＝[diag(σ1,σ2,...,σL),0]∈RL×(N-L+1) (4)
V＝[v1 v2 ... vN-L+1]∈R(N-L+1)×(N-L+1) (5)
Wherein U 1、V1 and Σ 1 represent a singular value unity orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace, U 2、V2 and Σ 2 represent a singular value unity orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace, R represents a real set, σ 1＞σ2＞...＞σL.
Substituting formula (2) into a deconvolution iterative formula, namely formula (6), to obtain formula (7):
where y represents the deconvolution iteration result, V L＝[v1 v2 ... vL.
In an embodiment, as shown in fig. 15, the first processing module 3 includes a second processing unit 311, a first computing unit 32, a second computing unit 33, and a third computing unit 34;
A second processing unit 311, configured to perform denoising processing on the unit orthogonal matrix by using sparse coding contraction, so as to obtain a denoised unit orthogonal matrix;
In this embodiment, to reduce the effect of noise on the result, sparse coding contraction is performed on each column x [ n ] in the VL-unit orthogonal matrix according to equation (8):
wherein, Each column vector in the V L unit orthogonal matrix after sparse coding shrinkage is represented, r represents a parameter for adjusting the noise reduction effect, and the greater r is, the more sparse the noise reduction result is; Sigma represents the standard deviation of noise, which is similar to the average absolute error of the unit orthogonal matrix; σ x represents the standard deviation of the identity orthogonal matrix. When the square root in the formula (8) is an imaginary number, Set to 0.
Normalizing the unit orthogonal matrix after sparse coding shrinkage according to a formula (9):
wherein, Each column vector in the normalized V L unit orthogonal matrix is represented.
A first calculation unit 32 for calculating a cyclic characteristic information ratio of the unit orthogonal matrix after the denoising process;
in this embodiment, in order to evaluate the information amount of each unit orthogonal matrix, the cyclic characteristic information ratio of each unit orthogonal matrix is used to evaluate the information amount of the unit orthogonal matrix in the specific implementation process.
Firstly, performing Hilbert on each unit orthogonal matrix according to a formula (10) and a formula (11) to obtain an analysis signal of the unit orthogonal matrix, and then calculating to obtain a square envelope spectrum:
the above is a specific calculation of the square envelope spectrum, wherein, Representing the result of the envelope processing, SES k represents the squared envelope spectrum.
Secondly, calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising processing according to the formula (12):
Wherein SES [ K ] max represents the largest value in SES [ K ], SES S (round (sK)) represents the round (sK) number in SES S after SES [ K ] is arranged from small to large, s represents a real number smaller than 1, K represents the length of the square envelope spectrum, namely K= (N-L+1)/2, K represents the kth point in the square envelope spectrum, and round (sK) represents rounding operation.
A second calculation unit 33, configured to calculate an iteration result of the denoised unit orthogonal matrix according to a comparison result of the cyclic characteristic information ratio and a preset cyclic characteristic information ratio threshold;
in this embodiment, a loop feature information ratio threshold p is preset, and the iteration result of each denoised unit orthogonal matrix is calculated based on the comparison result of the loop feature information ratio and the preset loop feature information ratio threshold according to formula (13):
Wherein V 1 represents the 1 st unit orthogonal matrix in V L unit orthogonal matrices, CCIR 1 represents the cyclic characteristic information ratio of the 1 st unit orthogonal matrix in V L unit orthogonal matrices, and x 1 represents the iteration result of the 1 st unit orthogonal matrix in V L unit orthogonal matrices.
A third calculation unit 34, configured to calculate a filtered signal according to the iteration result.
In this embodiment, the final filtered signal is obtained according to equation (14):
The above is a calculation process of the filtered signal, and x re represents the finally obtained filtered signal.
In an embodiment, as shown in fig. 15, the diagnostic module 5 includes an extraction unit 51 and a diagnostic unit 52;
an extracting unit 51 for extracting a characteristic frequency of a rolling bearing composite fault according to an envelope spectrum;
And a diagnostic unit 52 for diagnosing the composite fault of the rolling bearing according to the characteristic frequency.
In this embodiment, envelope analysis is performed on the filtered signal x re, and an envelope spectrum is obtained, so that the characteristic frequency of the rolling bearing composite fault is extracted, and finally the rolling bearing composite fault is identified according to the characteristic frequency.
In the specific implementation process, under the condition that the extracted characteristic frequency of the composite fault of the rolling bearing is consistent with the fault frequency of the rolling bearing, the composite fault of the rolling bearing is identified, the self-adaptive extraction of the composite fault characteristic of the rolling bearing is realized, and the diagnosis of the composite fault of the rolling bearing based on multi-period blind deconvolution is further realized.
The embodiment is derived from the traditional minimum entropy deconvolution, but breaks through the limitation of optimizing an objective function of a deconvolution method, decomposes a phase space formed by collected vibration signals through SVD, screens an effective unit orthogonal matrix, and finally runs to realize the feature extraction of the composite fault; the information quantity of the singular value unit orthogonal matrix is used as an index to establish a screening criterion, and a reconstruction component signal is not required to be screened, so that the influence of energy difference on the information quantity is eliminated; the method has the advantages that extra fault characteristic periods are not required to be provided, the self-adaptive extraction of the composite fault characteristics of the rolling bearing is realized according to weak fault characteristics with circulating characteristic information richer than reinforcing information, and the diagnosis of the composite fault of the rolling bearing based on multi-period blind deconvolution is further realized.
The following description is made in connection with specific examples:
For example, as shown in fig. 5, a certain locomotive bearing test bed is taken as an example, and the test bed is composed of a hydraulic motor, a driving wheel, a bearing, a locomotive wheel and the like, wherein the hydraulic motor drives the driving wheel, the driving wheel is contacted with an outer ring of the bearing and drives the outer ring of the bearing to move, an inner ring of the bearing is fixed on an axle of the locomotive wheel pair, and an acceleration sensor is fixed at one end of the bearing and is used for measuring vibration signals of the bearing.
In a specific implementation process, a vibration signal of a certain locomotive axle test bed is collected through an acceleration sensor, and the collected vibration signal is subjected to truncation and mean value removal processing to obtain a vibration signal x (N) subjected to truncation and mean value removal processing, for example, the sampling frequency is set to be 76.8kHz, the sampling duration is set to be 0.5s, the length N of the vibration signal is calculated to be 38400, the processed vibration signal x (N) is shown in fig. 6, and the envelope spectrum of the vibration signal x (N) is shown in fig. 7;
the iteration number k=100 and the filter length l=30 are preset, and then the toeplitz matrix X 0 is generated by using the vibration signal X (n) according to the formula (1):
Singular value decomposition is used on the generated toeplitz matrix X 0 to obtain a unit orthogonal matrix U, V and a diagonal matrix Σ, and the matrix is divided into two parts according to the filter length l=30 as shown in formula (2):
wherein,
U＝[u1 u2 ... u30]∈R30×30 (3)
∑＝[diag(σ1,σ2,...,σ30),0]∈R30×38370) (4)
V＝[v1 v2 ... v38370]∈R38370×38370) (5)
Wherein U 30、V30 and Σ 30 represent a singular value unit orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace formed by the first 30 items of each matrix, U 38370、V38370 and Σ 38370 represent a singular value unit orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace remaining in each matrix, and R represents a real number set and σ 1＞σ2＞...＞σ30.
Substituting formula (2) into a deconvolution iterative formula, namely formula (6), to obtain formula (7):
where y represents the deconvolution iteration result, V 30＝[v1 v2 ... v30.
To reduce the effect of noise on the result, each column x [ n ] in the V 30 unit orthogonal matrix is sparse coded and shrunk according to equation (8):
wherein, Each column vector in the V 30 unit orthogonal matrix after sparse coding shrinkage is represented, r represents a parameter for adjusting the noise reduction effect, and the greater r is, the more sparse the noise reduction result is, and in this embodiment r is set to 3; Sigma represents the standard deviation of noise, which is similar to the average absolute error of the unit orthogonal matrix; σ x represents the standard deviation of the identity orthogonal matrix. When the square root in the formula (8) is an imaginary number, Set to 0.
Normalizing the unit orthogonal matrix after sparse coding shrinkage according to a formula (9):
wherein, Each column vector in the normalized V 30 unit orthogonal matrix is represented.
In order to evaluate the information amount of each unit orthogonal matrix, the cyclic characteristic information ratio of each unit orthogonal matrix is used to evaluate the information amount of the unit orthogonal matrix.
Firstly, performing Hilbert on each unit orthogonal matrix according to a formula (10) and a formula (11) to obtain an analysis signal of the unit orthogonal matrix, and then calculating to obtain a square envelope spectrum:
the above is a specific calculation of the square envelope spectrum, wherein, Representing the result of the envelope processing, SES is the squared envelope spectrum.
Secondly, calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising processing according to the formula (12):
Wherein SES [ K ] max represents the largest value in SES [ K ], SES S (round (sK)) represents the round (sK) number in SES S after the SES [ K ] are arranged from small to large, s represents a real number smaller than 1, K represents the length of the square envelope spectrum, i.e., k=19185, K represents the kth point in the square envelope spectrum, and round (sK) represents performing rounding operation. The cyclic characteristic information of each unit orthogonal matrix of V 30 is shown in fig. 8.
A loop characteristic information ratio threshold p=4 is preset, and the iteration result of each denoising unit orthogonal matrix is calculated according to the comparison result of the loop characteristic information ratio and the preset loop characteristic information ratio threshold based on a formula (13):
Wherein V 1 represents the 1 st unit orthogonal matrix in V 30 unit orthogonal matrices, CCIR 1 represents the cyclic characteristic information ratio of the 1 st unit orthogonal matrix in V 30 unit orthogonal matrices, and x 1 represents the iteration result of the 1 st unit orthogonal matrix in V 30 unit orthogonal matrices.
Obtaining a final filtered signal according to equation (14):
The above is a calculation process of the filtered signal, and x re represents the finally obtained filtered signal.
In this embodiment, envelope analysis is performed on the filtered signal x re, and an envelope spectrum is obtained, so as to extract the fault characteristic frequency of the rolling bearing, and finally, the composite fault of the rolling bearing is identified according to the characteristic frequency.
In this embodiment, fig. 9 is a result of deconvolution filtering of the maximum correlation kurtosis when the input period is the inner ring fault feature frequency, fig. 10 is a corresponding vibration signal envelope spectrum, fig. 11 is a result of deconvolution filtering of the maximum correlation kurtosis when the input period is the outer ring fault feature frequency, and fig. 12 is a corresponding vibration signal envelope spectrum. As can be seen from fig. 9, 10, 11, and 12, the fault impact signal cannot be extracted effectively when the input period is the outer ring characteristic frequency, and the fault characteristic frequency is difficult to find in the envelope spectrum, so that a plurality of fault characteristic signals cannot be extracted adaptively. Fig. 13 is a graph of vibration signals after multi-period blind deconvolution, and fig. 14 is a graph of envelope spectra of corresponding vibration signals after multi-period blind deconvolution. As can be seen from fig. 13 and 14, the extraction method adopted in the present application significantly and completely extracts a plurality of fault vibration signals, and significantly discovers the fault characteristic frequencies of the inner ring and the outer ring in the envelope spectrum, compared with the conventional methods of fig. 9, 10, 11 and 12. The method effectively verifies that the self-adaptive extraction of the composite fault characteristics of the rolling bearing can be realized without priori knowledge.
In the embodiment, the acquired vibration signal of the rolling bearing is decomposed to obtain a unit orthogonal matrix; denoising and evaluating the unit orthogonal matrix to obtain a filtered signal; and then diagnosing the composite fault of the rolling bearing according to an envelope spectrum obtained after envelope analysis processing is carried out on the filtering signal, thereby enhancing the weak fault characteristics in the composite fault characteristics, realizing self-adaptive extraction of the composite fault characteristics of the rolling bearing, and further realizing diagnosis of the composite fault of the rolling bearing based on multi-period blind deconvolution.
Example 3
Fig. 16 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of diagnosing a rolling bearing composite failure of embodiment 1 when executing the program. The electronic device 30 shown in fig. 16 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
As shown in fig. 16, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be a server device, for example. Components of electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, a bus 33 connecting the different system components, including the memory 32 and the processor 31.
The bus 33 includes a data bus, an address bus, and a control bus.
Memory 32 may include volatile memory such as Random Access Memory (RAM) 321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
Memory 32 may also include a program/utility 325 having a set (at least one) of program modules 324, such program modules 324 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 31 executes various functional applications and data processing, such as a method of diagnosing a rolling bearing composite failure of embodiment 1 of the present invention, by running a computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface 35. Also, model-generating device 30 may also communicate with one or more networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet, via network adapter 36. As shown in fig. 16, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the model-generating device 30, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present invention. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of diagnosing a rolling bearing composite failure of embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention can also be realized in the form of a program product comprising program code for causing a terminal device to carry out the method for diagnosing a composite fault of a rolling bearing implementing embodiment 1, when the program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device, partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (6)
1. A method of diagnosing a composite fault of a rolling bearing, the method comprising:
acquiring a vibration signal of a rolling bearing;
Decomposing the vibration signal to obtain an identity orthogonal matrix;
Denoising and evaluating the unit orthogonal matrix to obtain a filtered signal;
Performing envelope analysis processing on the filtered signal to obtain an envelope spectrum;
diagnosing the rolling bearing composite fault according to the envelope spectrum;
the step of decomposing the vibration signal to obtain a unit orthogonal matrix includes:
Cutting off and removing the mean value of the vibration signal to obtain a vibration signal after cutting off and removing the mean value;
Generating a toeplitz matrix by using the vibration signals subjected to the truncation and mean value removal, presetting iteration times k and filter length L, wherein the formula is as follows:
Wherein, X 0 represents a Toeplitz matrix, X N represents the last element of the vibration signal time sequence, and N represents the length of the vibration signal;
Decomposing the Toeplitz matrix by adopting singular values to obtain the unit orthogonal matrix U, V and the diagonal matrix sigma, and dividing the matrix into two parts according to the length L of the filter, wherein the formula is as follows:
wherein,
U＝[u1 u2 … uL]∈RL×L (3)；
Σ＝[diag(σ1,σ2,…,σL),0]∈RL×(N-L+1) (4)；
V＝[v1 v2 … vN-L+1]∈R(N-L+1)×(N-L+1) (5)；
Wherein U 1、V1 and Σ 1 represent a singular value unity orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace, U 2、V2 and Σ 2 represent a singular value unity orthogonal matrix and a singular value diagonal matrix corresponding to the noise signal subspace, R represents a real set, σ 1>σ2>…>σL;
substituting formula (2) into a deconvolution iterative formula, namely formula (6), to obtain formula (7):
Wherein y represents a deconvolution iteration result, V L＝[v1 v2…vL ];
The step of denoising and evaluating the unit orthogonal matrix to obtain a filtered signal includes:
carrying out denoising treatment on the unit orthogonal matrix by adopting sparse coding shrinkage to obtain a denoised unit orthogonal matrix, and carrying out sparse coding shrinkage on each column x [ n ] in the V L unit orthogonal matrix according to a formula (8):
wherein, Each column vector in the V L unit orthogonal matrix after sparse coding shrinkage is represented, r represents a parameter for adjusting the noise reduction effect, and the greater r is, the more sparse the noise reduction result is; Sigma represents the standard deviation of noise, which is similar to the average absolute error of the unit orthogonal matrix; σ x represents the standard deviation of the unit orthogonal matrix, when the square root in equation (8) is an imaginary number, Set to 0;
Normalizing the unit orthogonal matrix after sparse coding shrinkage according to a formula (9):
wherein, Representing each column vector in the normalized V L unit orthogonal matrix;
calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising treatment;
Performing Hilbert on each unit orthogonal matrix according to a formula (10) and a formula (11) to obtain an analysis signal of the unit orthogonal matrix, and then calculating to obtain a square envelope spectrum:
wherein, Representing the result of the envelope processing, SES [ k ] representing the square envelope spectrum;
Calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising process according to the formula (12):
SES [ K ] max represents the largest value in SES [ K ], SES S (round (sK)) represents the round (sK) number in SES S after SES [ K ] is arranged from small to large, s represents a real number smaller than 1, K represents the length of the square envelope spectrum, K= (N-L+1)/2, K represents the kth point in the square envelope spectrum, and round (sK) represents performing rounding operation;
Calculating according to the comparison result of the cyclic characteristic information ratio and a preset cyclic characteristic information ratio threshold value to obtain an iteration result of the unit orthogonal matrix after the denoising processing;
And calculating the filtering signal according to the iteration result.
2. The method of diagnosing a composite fault of a rolling bearing according to claim 1, wherein the step of diagnosing the composite fault of the rolling bearing based on the envelope spectrum includes:
Extracting the characteristic frequency of the rolling bearing composite fault according to the envelope spectrum;
and diagnosing the composite fault of the rolling bearing according to the characteristic frequency.
3. A diagnosis system of a composite fault of a rolling bearing, characterized in that the diagnosis system comprises an acquisition module, a decomposition module, a first processing module, a second processing module and a diagnosis module, wherein the diagnosis method of the composite fault of the rolling bearing is applied;
the acquisition module is used for acquiring vibration signals of the rolling bearing;
The decomposition module is used for decomposing the vibration signal to obtain a unit orthogonal matrix;
The first processing module is used for carrying out denoising and evaluation processing on the unit orthogonal matrix so as to obtain a filtered signal;
The second processing module is used for carrying out envelope analysis processing on the filtered signal so as to obtain an envelope spectrum;
the diagnosis module is used for diagnosing the rolling bearing composite fault according to the envelope spectrum;
The decomposition module comprises a first processing unit, a generating unit and a decomposition unit;
the first processing unit is used for carrying out truncation and mean value removal processing on the vibration signals so as to obtain truncated and mean value removed vibration signals;
The generating unit is used for generating a toeplitz matrix by using the vibration signals subjected to the truncation and mean removal processing;
the decomposition unit is used for decomposing the Toeplitz matrix by adopting singular values so as to obtain the unit orthogonal matrix;
The first processing module comprises a second processing unit, a first computing unit, a second computing unit and a third computing unit;
The second processing unit is used for carrying out denoising processing on the unit orthogonal matrix by adopting sparse coding shrinkage so as to obtain a denoised unit orthogonal matrix;
The first calculation unit is used for calculating the cyclic characteristic information ratio of the unit orthogonal matrix after the denoising treatment;
the second calculation unit is used for calculating and obtaining an iteration result of the unit orthogonal matrix after the denoising process according to a comparison result of the cyclic characteristic information ratio and a preset cyclic characteristic information ratio threshold;
the third calculation unit is configured to calculate the filtered signal according to the iteration result.
4. A diagnostic system for a composite fault of a rolling bearing according to claim 3, wherein the diagnostic module comprises an extraction unit and a diagnostic unit;
The extraction unit is used for extracting the characteristic frequency of the rolling bearing composite fault according to the envelope spectrum;
And the diagnosis unit is used for diagnosing the composite fault of the rolling bearing according to the characteristic frequency.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a method for diagnosing a composite fault of a rolling bearing according to any one of claims 1-2 when executing the computer program.
6. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a method for diagnosing a composite fault of a rolling bearing according to any one of claims 1-2.
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