CN114964769A - Wind power gear box vibration signal fault diagnosis method - Google Patents
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Abstract
The invention relates to a wind power gear box vibration signal fault diagnosis method, which comprises the following steps: obtaining an original vibration signal; constructing a Hankel matrix according to the original vibration signal, and performing singular value decomposition on the Hankel matrix to obtain a singular value sequence matrix; determining a hard threshold value of a singular value sequence matrix, and processing the singular value sequence matrix by using a mixed threshold singular value decomposition noise reduction algorithm based on the hard threshold value; reducing the processed singular value sequence matrix into a Hankel matrix, and reducing into a vibration signal subjected to noise reduction; filtering the vibration signal subjected to noise reduction; and carrying out envelope analysis on the filtered signal to obtain fault characteristic frequency. The method adopts a hard threshold self-adaptive selection algorithm of singular value decomposition, solves the problems of strong subjectivity, weak self-adaptability and easy signal loss of threshold selection of the traditional hard threshold singular value decomposition noise reduction method, and aims to effectively extract the fault characteristics of the wind power gear box in a strong noise environment.
Description
Technical Field
The invention relates to the field of mechanical equipment state monitoring, in particular to a vibration signal fault diagnosis method for a wind power gear box.
Background
The wind turbine generator works in a severe environment all the year round, most of equipment is installed at high altitude, serious production accidents can be caused once a fault occurs, and the gear is used as a main component in the wind power transmission system and is often high in fault occurrence rate, so that the gear is timely and effectively detected and diagnosed, and the gear has important significance on normal operation of the wind power transmission system.
The existing gear diagnosis method is mainly based on vibration signals, and fault diagnosis is carried out on gear parts by using a signal processing technology and a machine learning algorithm. The common machine learning algorithm such as a support vector machine, a random forest, a neural network and the like needs to spend larger calculation time, has slow convergence and weak anti-noise performance, and needs larger data samples, and the application effect of the machine learning method on the gear fault diagnosis is limited by the factors. Common signal processing technologies include decomposition and reconstruction of signals, signal noise reduction algorithms and the like, such as variational modal decomposition, empirical modal decomposition and wavelet decomposition, the decomposition and noise reduction methods have the problems of end point effect, modal aliasing, poor adaptability of signal types and the like, in addition, the running environment of a unit is noisy, the rotating speed of a gear is generally low, so that the vibration signal excited by the fault in the gear box is transmitted to a box body to be shown, the attenuation is large, the fault signal with the characteristics of periodicity and impact is difficult to find from the time domain and the frequency domain, and the diagnosis accuracy is limited.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a wind power gear box vibration signal fault diagnosis method, aiming at effectively extracting the fault characteristics of the wind power gear box in a strong noise environment.
The technical scheme adopted by the invention is as follows:
a wind power gear box vibration signal fault diagnosis method comprises the following steps:
s1, obtaining an original vibration signal;
s2, constructing a Hankel matrix according to the original vibration signal, and performing singular value decomposition on the Hankel matrix to obtain a singular value sequence matrix diag (sigma-sigma) 1 ，σ 2 ，σ 3 …σ N )，σ N Is the Nth singular value;
s3, determining a hard threshold K of the singular value sequence matrix, and processing the singular value sequence matrix by using a mixed threshold singular value decomposition noise reduction algorithm based on the hard threshold K;
the determining the hard threshold of the singular value sequence matrix by adopting a hard threshold selection algorithm comprises the following steps:
with a continuously decaying screening value Y i As a standard, Y i ＝Y i-1 -i λ, i being the number of decays, λ being the decay step;
reference value Y for each time i Counting the newly added value greater than Y i Number of singular values of C i ；
If at the nth time, C i Is greater than the set value U, all C's from the n-1 th time to the first time i (i ═ 1, 2 … n-1) to obtain a hard threshold K;
s4, restoring the processed singular value sequence matrix into a Hankel matrix, and then restoring the Hankel matrix into a vibration signal subjected to noise reduction;
s5, filtering the vibration signal subjected to noise reduction;
and S6, carrying out envelope analysis on the filtered signal to obtain fault characteristic frequency.
The further technical scheme is as follows:
in step S3, the processing the singular value sequence matrix by using a mixed threshold singular value decomposition noise reduction algorithm based on the hard threshold K includes:
determining a singular value sigma corresponding to a hard threshold K K For the singular value sequence diag (sigma) 1 ，σ 2 ，σ 3 …σ N ) Middle greater than sigma K Is reserved for singular values of less than or equal to sigma K Singular value of i Using an optimum singular value contractor H * Performing a shrinking process, an optimal singular value shrinker H * The expression of (a) is as follows:
wherein, K +1 is more than or equal to i and less than or equal to N, and D is the ratio of the number of rows to the number of columns of the Hankel matrix.
In step S2, a Hankel matrix is constructed according to the original vibration signal, and singular value decomposition is performed on the Hankel matrix to obtain a singular value sequence matrix diag (σ) 1 ，σ 2 ，σ 3 …σ N ) The method comprises the following steps:
s21, for the original vibration signal sequence (x) with the length of N 1 ，x 2 ，x 3 …x N ) Taking x 1 ，x 2 ，x 3 …x m As the first row of the matrix, x 2 ，x 3 ，x 4 ，…x m+1 The second row of the matrix is obtained by analogy until a Hankel matrix with dimension of m multiplied by m is obtained, wherein m is less than 0.5N;
s22, dividing the constructed Hankel matrix by
c is the noise intensity;
s23, performing singular value decomposition on the Hankel matrix: hankel Pdiag (sigma) 1 ，σ 2 ，σ 3 …σ N )Q T Three matrices are obtained, Hankel for Hankel matrix, diag (σ) 1 ，σ 2 ，σ 3 …σ N ) Is a singular value sequence matrix, P is a left singular value matrix, Q T Is a matrix of right singular values.
In step S4, the reducing the processed singular value sequence matrix into a Hankel matrix includes:
the processed singular value sequence matrix is processed
Multiplying by a matrix of left and right singular values and multiplying by
Reduction to Hankel matrix:
Hankel * in order to restore the Hankel matrix,
is the nth singular value.
In step S5, the filtering the noise-reduced vibration signal includes:
by kurtosis index K v As evaluation basis, sliding a sliding window with length of l by one step length each time, and filtering the signal sequence in the sliding window by a suppression function once each time the sliding window is slid until the sliding window is slid to the end of the sequence, and calculating a kurtosis index K v If not, updating the parameters of the inhibition function and continuing the operation until K v Convergence or stopping upon reaching a maximum number of iterations, K v The expression of (a) is as follows:
wherein x is N (n) is the nth sequence point in the vibration signal sequence after noise reduction,
the average amplitude of the whole vibration signal sequence is obtained, and N is the length of the vibration signal subjected to noise reduction;
the expression of the suppression function is as follows:
wherein x is l (n) is the nth sequence of the signal sequence located in the sliding windowColumn points; | x l (n) | represents x l (n) amplitude, max (| x) l (n) |)) represents | x l (n) the maximum value of |; subscript l represents the sliding window length, l ═ N/8;
(σmax(|x l (n)|)-|x l (n)|，0) + ＝max(σmax(|x l (n)|)-|x l (n)|，0)；
σ is attenuation coefficient, σ is 1-1/(1+ e) -0.5k+2.5 ) And k represents the first k iteration.
Step S6 specifically includes: and performing Hilbert transform on the filtered signal to obtain a module of an analysis signal, and then obtaining a Fourier spectrum of the analysis signal to obtain fault characteristic frequency.
The invention has the following beneficial effects:
1. the invention adopts a new hard threshold self-adaptive selection algorithm of singular value decomposition, and solves the problems of strong subjectivity, weak self-adaptability and easy signal characteristic loss in threshold selection of the traditional noise reduction method of the singular value decomposition by using the hard threshold.
2. The invention combines the traditional hard threshold noise reduction algorithm with a non-equivalent optimal shrinkage soft threshold singular value decomposition noise reduction method, and obviously improves the noise reduction effect of signals.
3. The invention provides an amplitude suppression algorithm for highlighting the periodic fault impact characteristics of a gear signal after noise reduction, and the fault characteristic frequency of a fault gear is obviously and effectively extracted by utilizing envelope analysis.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a time domain waveform and an envelope spectrum of an original vibration signal extracted according to an embodiment of the present invention.
Fig. 3 shows the time domain waveform and the envelope spectrum of the processed signal according to the embodiment of the invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
Referring to fig. 1, the method for diagnosing the vibration signal fault of the wind power gearbox of the embodiment includes:
s1, obtaining an original vibration signal:
for a fixed-axis gear box, the characteristic frequency of local fault of a gear is the rotating frequency of a fault gear, and the rotating speed and the number of teeth of a driving wheel are respectively Z 1 (r/min)、N 1 With a fault characteristic frequency of Z 1 /60, the number of driven wheels is N 2 With a fault characteristic frequency of Z 1 N 1 /(60N 2 )；
S2, constructing a Hankel matrix according to the original vibration signal, and performing singular value decomposition on the Hankel matrix to obtain a singular value sequence matrix diag (sigma-sigma) 1 ，σ 2 ，σ 3 …σ N )，σ N The method specifically includes, for an nth singular value:
s21 for original vibration signal sequence (x) with length N8000 1 ，x 2 ，x 3 …x N ) Taking x 1 ，x 2 ，x 3 …x m As the first row of the matrix, x 2 ，x 3 ，x 4 ，…x m+1 The second row of the matrix is obtained, and the rest is repeated until a Hankel matrix with m multiplied by m dimensions is obtained, m is less than 0.5N, and the specific m is 2000;
s22, dividing the constructed Hankel matrix by
c is the noise intensity, and the standard deviation of the original vibration signal can be approximately regarded as c in a strong noise environment;
s23, performing singular value decomposition on the Hankel matrix: hankel Pdiag (σ) 1 ，σ 2 ，σ 3 …σ N )Q T Three matrices are obtained, Hankel for Hankel matrix, diag (σ) 1 ，σ 2 ，σ 3 …σ N ) Is a singular value sequence matrix, P is a left singular value matrix, Q T Is a matrix of right singular values.
S3, determining a hard threshold K of the singular value sequence matrix, and processing the singular value sequence matrix by using a mixed threshold singular value decomposition noise reduction algorithm based on the hard threshold K;
the method for determining the hard threshold of the singular value sequence matrix by adopting the hard threshold selection algorithm comprises the following steps:
with a continuously decaying screening value Y i As a standard, Y i ＝Y i-1 -i2, i being the number of decays and λ being the decay step;
reference value Y for each time i Counting the newly added value greater than Y i Number of singular values of C i ；
If at the nth time, C i Is greater than the set value U, all C's from the n-1 th time to the first time i (i ═ 1, 2 … n-1) to obtain a hard threshold K; in the present embodiment, preferably, U-5;
the processing the singular value sequence matrix by using a mixed threshold singular value decomposition noise reduction algorithm based on the hard threshold K comprises:
determining a singular value sigma corresponding to a hard threshold K K For the singular value sequence diag (sigma) 1 ，σ 2 ，σ 3 …σ N ) In is greater than sigma K Is reserved for singular values of less than or equal to sigma K Singular value of i Using optimal singular value contractors H * Performing a shrinking process, an optimal singular value shrinker H * The expression of (a) is as follows:
where K +1 is not less than i and not more than N, D is a ratio of the number of rows to the number of columns of the Hankel matrix, and D is 1 in this embodiment.
The singular value sequence matrix after comprehensive processing by the mixed threshold method is as follows:
diag(σ 1 ，σ 2 …σ K ，H * (σ K+1 )，H * (σ K+2 )…H * (σ N ))
in the hard threshold selection algorithm provided by the embodiment, the selected hard threshold can be used as an effective basis for processing a singular value sequence matrix, and the mixed threshold singular value decomposition noise reduction algorithm provided by the embodiment is equivalent to combining the hard threshold noise reduction algorithm with a nonequivalent optimal shrinkage soft threshold singular value decomposition noise reduction method, so that singular values in the first half of the hard threshold correspond to periodic principal components of signals, and singular values in the second half of the hard threshold are smaller and more gently changed correspond to noise components of signals, and thus the noise reduction effect is remarkably improved.
S4, reducing the processed singular value sequence matrix into a Hankel matrix, and then reducing the Hankel matrix into a vibration signal after noise reduction, wherein the method specifically comprises the following steps:
the processed singular value sequence matrix diag (sigma) 1 ，σ 2 …σ K ，H * (σ K+1 )，H * (σ K+2 )…H * (σ N ) Multiply by the left and right singular value matrices and multiply by
Reduction to Hankel matrix:
wherein, Hankel * In order to restore the Hankel matrix,
for the Nth singular value, the processed singular value sequence matrix diag (σ) 1 ，σ 2 …σ K ，H * (σ K+1 )，H * (σ K+2 )…H * (σ N ) Can be recorded as
S5, filtering the vibration signal after noise reduction, specifically comprising:
by kurtosis index K v As evaluation basis, a sliding window with length of l is used, each time the sliding window is slid by one step length, and each sliding is carried out, the signal sequence x in the sliding window is processed l (n) performing a filtering operation through a suppression function until the sequence is slid to the end of the sequence, and calculating a kurtosis index K v If not, updating the parameters of the inhibition function and continuing the operation until K v Convergence or stopping upon reaching a maximum number of iterations, K v The expression of (c) is as follows:
wherein, x N (n) is the nth sequence point in the vibration signal sequence after noise reduction,
the average amplitude of the whole vibration signal sequence is obtained, and N is the length of the vibration signal subjected to noise reduction;
the expression of the suppression function is as follows:
wherein x is l (n) is the nth sequence point of the signal sequence located within the sliding window; | x l (n) | represents x l (n) amplitude, max (| x) l (n) |) represents | x l (n) | maximum value; subscript l represents the sliding window length, l ═ N/8;
(σmax(|x l (n)|)-|x l (n)|，0) + ＝max(σmax(|x l (n)|)-|x l (n)|，0)；
σ is attenuation coefficient, σ is 1-1/(1+ e) -0.5k+2.5 ) And k represents the first k iteration.
Sigma is the parameter, and the kurtosis index K is calculated v If the time does not converge, updating sigma, continuing sliding and calculating until K v Converge or satisfy the maximum number of iterations.
Compared with the traditional kurtosis, the embodiment adopts the kurtosis index which is a dimensionless index reflecting the deviation degree of the signal from the normal distribution, is particularly sensitive to the impact component in the signal, and has a value which is independent of the rotating speed of the input shaft, the sizes of parts and components of the gearbox and the load.
The amplitude suppression algorithm provided by the embodiment can be used for suppressing the interference of noise and clutter around the impulse signal section after singular value decomposition and noise reduction, and better highlighting the periodic fault impulse characteristics of the gear signal. Compared with some traditional methods for highlighting the characteristics, such as Teager energy operators, the method has better fidelity to the signal impact part, and can highlight the impact part better and inhibit noise and clutter ground interference.
S6, carrying out envelope analysis on the filtered signal to obtain fault characteristic frequency, and specifically comprising the following steps:
and performing Hilbert transform on the filtered signal to obtain a module of an analysis signal, and then obtaining a Fourier spectrum of the analysis signal to obtain fault characteristic frequency.
The technical solution of the present application will be described below with reference to specific test data.
A wind power gear box vibration signal fault diagnosis method comprises the following steps:
s1, obtaining a vibration signal of the wind power gear box:
the data is acquired by a wind power transmission test bed of a national engineering research center for intelligent measurement and control and safe operation of large power generation equipment of the institute of energy and environment of southeast university, and the whole device mainly comprises the transmission test bed, an acceleration sensor, a data acquisition system and a computer. The test bed part comprises a driving motor, a main shaft, a torque and rotating speed sensor, a single-stage cylindrical gear box, a coupler and a load motor. Different rotating speeds (50-1000 r/min) can be obtained by adjusting the variable-frequency driving motor. The sampling frequency is 10kHz, the gear rotating speed is 800r/min, the number of teeth is 18, the fault gear is a driven gear, the fault type is a broken tooth fault, the number of teeth of the gear is 58, and the fault characteristic frequency of the fault gear is 4.13Hz through calculation. A continuous 8000 data points are randomly extracted from 234827 data points obtained by sampling, namely the length N of a gear vibration signal to be analyzed is 8000, and meanwhile, Gaussian noise with certain intensity is added to the vibration signal in order to simulate a strong noise environment in wind power actual work. Fig. 2 (a) and (b) are respectively time domain waveform and envelope spectrum of the gear after noise addition, and it can be seen that due to interference of noise, an obvious fault impact characteristic cannot be observed from the time domain diagram, and a fault characteristic frequency of the gear cannot be observed in the envelope spectrum.
S2, a 2000 × 2000 Hankel matrix is constructed based on the processed gearbox vibration signals, the number m of matrix lines is 2000, the noise variance of the signals is estimated, the estimated value c is 1, and the Hankel matrix is subjected to singular value decomposition to obtain a singular value sequence matrix.
And S3, processing the singular value sequence by using a mixed threshold singular value decomposition noise reduction algorithm based on the selected hard threshold, wherein the selected hard threshold K is 121.
S4, reducing the processed singular value sequence matrix into a Hankel matrix, and multiplying the Hankel matrix by the Hankel matrix
And finally, restoring the vibration signals into noise-reduced vibration signals.
S5, filtering the signal subjected to noise reduction through an amplitude suppression filter;
the present embodiment sets the maximum number of iterations K to 30, and when K to 12, the kurtosis index K is set to v Starting convergence, and finally taking K as 30 v 29.313 for the corresponding signal.
And S6, carrying out envelope analysis on the processed signal to obtain fault characteristic frequency.
In fig. 3, (a) and (b) are respectively time domain waveform and envelope spectrum of the processed fault gear signal, and it is obvious that the impact signal component is observed from fig. 3(a), and the interval between the impact signals is about 0.25s, and the calculated frequency is: the 1/0.25-4 Hz frequency is basically consistent with the fault characteristic frequency of 4.13Hz of the fault gear. In fig. 3(b), a 4.13Hz fault characteristic frequency and its frequency doubling component can be very obviously observed, which proves that the analysis method of the present application can effectively diagnose the fault of the wind power gear.
Those of ordinary skill in the art will understand that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A wind power gear box vibration signal fault diagnosis method is characterized by comprising the following steps:
s1, obtaining an original vibration signal;
s2, constructing a Hankel matrix according to the original vibration signal, and performing singular value decomposition on the Hankel matrix to obtain a singular value sequence matrix diag (sigma-sigma) 1 ，σ 2 ，σ 3 …σ N )，σ N Is the Nth singular value;
s3, determining a hard threshold K of the singular value sequence matrix, and processing the singular value sequence matrix by using a mixed threshold singular value decomposition noise reduction algorithm based on the hard threshold K;
the determining the hard threshold of the singular value sequence matrix by adopting a hard threshold selection algorithm comprises the following steps:
with a continuously decaying screening value Y i As a standard, Y i ＝Y i-1 -i λ, i being the number of decays, λ being the decay step;
reference value Y for each time i Counting the newly added value greater than Y i Number of singular values of C i ；
If at the nth time, C i Is greater than the set value U, all C's from the n-1 th time to the first time i (i ═ 1, 2 … n-1) to obtain a hard threshold K;
s4, restoring the processed singular value sequence matrix into a Hankel matrix, and then restoring the Hankel matrix into a vibration signal subjected to noise reduction;
s5, filtering the vibration signal subjected to noise reduction;
and S6, carrying out envelope analysis on the filtered signal to obtain fault characteristic frequency.
2. The method for diagnosing the vibration signal fault of the wind turbine gearbox according to claim 1, wherein in step S3, the processing the singular value sequence matrix by using a hybrid threshold singular value decomposition noise reduction algorithm based on the hard threshold K comprises:
determining a singular value sigma corresponding to a hard threshold K K For the singular value sequence diag (sigma) 1 ，σ 2 ，σ 3 …σ N ) Middle greater than sigma K Is reserved for singular values of less than or equal to sigma K Singular value of i Using optimal singular value contractors H * Performing a shrinking process, an optimal singular value shrinker H * The expression of (a) is as follows:
wherein, K +1 is more than or equal to i and less than or equal to N, and D is the ratio of the number of rows to the number of columns of the Hankel matrix.
3. The wind power gearbox vibration signal fault diagnosis method as claimed in claim 1, wherein in step S2, a Hankel matrix is constructed according to the original vibration signal, and singular value decomposition is performed on the Hankel matrix to obtain a singular value sequence matrix diag (σ) 1 ，σ 2 ，σ 3 …σ N ) The method comprises the following steps:
s21, for the original vibration signal sequence (x) with the length of N 1 ，x 2 ，x 3 …x N ) Taking x 1 ，x 2 ，x 3 …x m As the first row of the matrix, x 2 ，x 3 ，x 4 ，…x m+1 The second row of the matrix is obtained by analogy until a Hankel matrix with dimension of m multiplied by m is obtained, wherein m is less than 0.5N;
s22, dividing the constructed Hankel matrix by
c is the noise intensity;
s23, performing singular value decomposition on the Hankel matrix: hankel Pdiag (σ) 1 ，σ 2 ，σ 3 …σ N )Q T Three matrices are obtained, Hankel for Hankel matrix, diag (σ) 1 ，σ 2 ，σ 3 …σ N ) Is a singular value sequence matrix, P is a left singular value matrix, Q T Is a matrix of right singular values.
4. The method for diagnosing the vibration signal fault of the wind power gearbox according to claim 3, wherein in the step S4, the step of reducing the processed singular value sequence matrix into a Hankel matrix comprises the following steps:
the processed singular value sequence matrix is processed
Multiplying by a matrix of left and right singular values and multiplying by
Reduction to Hankel matrix:
Hankel * in order to restore the Hankel matrix,
is the nth singular value.
5. The wind power gearbox vibration signal fault diagnosis method as claimed in claim 1, wherein in step S5, the filtering the noise-reduced vibration signal comprises:
by kurtosis index K v As evaluation basis, sliding a sliding window with length of l by one step length each time, and filtering the signal sequence in the sliding window by a suppression function once each time the sliding window is slid until the sliding window is slid to the end of the sequence, and calculating a kurtosis index K v If not, updating the parameters of the inhibition function and continuing the operation until K v Convergence or stopping upon reaching a maximum number of iterations, K v The expression of (a) is as follows:
wherein x is N (n) is the nth sequence point in the vibration signal sequence after noise reduction,
the average amplitude of the whole vibration signal sequence is obtained, and N is the length of the vibration signal subjected to noise reduction;
the expression of the suppression function is as follows:
wherein x is l (n) is the nth sequence point of the signal sequence located within the sliding window; | x l (n) | represents x l (n) amplitude, max (| x) l (n) |) represents | x l (n) the maximum value of |; subscript l represents the sliding window length, l ═ N/8;
(σmax(|x l (n)|)-|x l (n)|，0) + ＝max(σmax(|x l (n)|)-|x l (n)|，0)；
σ is attenuation coefficient, σ is 1-1/(1+ e) -0.5k+2.5 ) And k represents the first k iteration.
6. The wind power gearbox vibration signal fault diagnosis method as claimed in claim 1, wherein step S6 specifically comprises: and performing Hilbert transform on the filtered signal to obtain a module of an analysis signal, and then obtaining a Fourier spectrum of the analysis signal to obtain fault characteristic frequency.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730199A (en) * | 2022-11-10 | 2023-03-03 | 天地(常州)自动化股份有限公司北京分公司 | Method and system for noise reduction and fault feature extraction of vibration signal of rolling bearing |
| CN116861219A (en) * | 2023-09-01 | 2023-10-10 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115730199A (en) * | 2022-11-10 | 2023-03-03 | 天地(常州)自动化股份有限公司北京分公司 | Method and system for noise reduction and fault feature extraction of vibration signal of rolling bearing |
| CN115730199B (en) * | 2022-11-10 | 2023-07-21 | 天地(常州)自动化股份有限公司北京分公司 | Rolling bearing vibration signal noise reduction and fault feature extraction method and system |
| CN116861219A (en) * | 2023-09-01 | 2023-10-10 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
| CN116861219B (en) * | 2023-09-01 | 2023-12-15 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
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