CN113657268A - Signal automatic decomposition method applied to wind turbine generator gearbox fault diagnosis - Google Patents

Signal automatic decomposition method applied to wind turbine generator gearbox fault diagnosis Download PDF

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CN113657268A
CN113657268A CN202110939983.5A CN202110939983A CN113657268A CN 113657268 A CN113657268 A CN 113657268A CN 202110939983 A CN202110939983 A CN 202110939983A CN 113657268 A CN113657268 A CN 113657268A
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CN113657268B (en
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胡忠忠
杨志勇
李志军
陈果
黎炯
曹玲燕
卢应强
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Guodian Nanjing Automation Haiji Technology Co ltd
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Abstract

The invention discloses a signal automatic decomposition method applied to fault diagnosis of a gearbox of a wind turbine generator, which comprises the following steps of: step 1: acquiring a fault vibration signal of a high-speed shaft gear of a gear box; step 2: VMD decomposition is carried out on the fault vibration signal, and the fault vibration signal is divided into a high-frequency signal and a low-frequency signal; and step 3: calculating a dependence index and a central frequency difference degree between the high-frequency signal and the low-frequency signal; and 4, step 4: determining a decomposition mode; and 5: updating the signal; step 6: the step 2 to the step 5 are circulated until the problems of under-decomposition and over-decomposition do not exist, and the iteration is terminated; and 7: and outputting all decomposition modes in the signal decomposition process to finish the automatic signal decomposition. The invention can realize the effect of automatic decomposition of the signal by depending on the index and the central frequency difference, and can select a modal signal which can represent the original signal to extract the fault characteristics after carrying out multi-dimensional evaluation on the decomposed mode, thereby achieving the purpose of fault diagnosis, improving the efficiency and having strong applicability.

Description

Signal automatic decomposition method applied to wind turbine generator gearbox fault diagnosis
Technical Field
The invention relates to the field of wind turbine generator gearbox fault diagnosis, in particular to a signal automatic decomposition method applied to wind turbine generator gearbox fault diagnosis.
Background
At present, the fault diagnosis of the wind turbine generator mainly aims at a transmission system, in particular a gear box, of the wind turbine generator, and the current diagnosis methods mainly comprise the following three methods: the method is based on a vibration signal analysis method, a fault mechanism modeling method and a machine learning method based on data driving.
In the fault mechanism-based modeling method, because the wind turbine generator has the characteristics of variable load, severe operating environment, high possibility of being influenced by environmental factors, nonlinear unit faults and the like, the knowledge of the fault mechanism is difficult to accurately describe by adopting some mathematical methods, and a uniform model is difficult to establish to explain the faults.
Although the machine learning method based on data driving shows strong calculation capability and feature extraction capability, the machine learning method usually needs to learn fault data, namely the fault data needs to be labeled and then taken as training data.
Based on vibration signal analysis, the diagnosis effect can be achieved only by acquiring vibration signals of the running of the wind turbine generator, processing the vibration signals and extracting fault features in the signals, the process is simple, the method has universality, and the method is the most widely and effectively applied method in the current wind turbine generator fault diagnosis process.
When the vibration signal is subjected to data processing, foreign scholars propose a fault diagnosis method of EMD (empirical mode decomposition) aiming at the nonlinearity and the non-stationarity of the fault vibration signal of the wind turbine generator gearbox, the EMD can adaptively decompose the complex vibration signal into a plurality of intrinsic mode function components, the separation of the fault vibration signal and the noise signal is realized, and the method is widely applied in the field of fault diagnosis. However, the EMD has modal aliasing and end-point effect, and is easy to generate false components when decomposing signals, thereby increasing the calculation cost and time. The VMD (spatial mode decomposition) is a signal estimation method, and its overall frame is a variation problem, and it adopts an alternating direction multiplier method to continuously update each mode and its center frequency, and gradually demodulates each mode to a corresponding fundamental frequency band, and finally each mode and a corresponding center frequency are extracted together.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a signal automatic decomposition method applied to the wind turbine generator gearbox fault diagnosis, which can achieve the effect of automatic decomposition of signals by depending on indexes and central frequency difference degrees, and can perform multi-dimensional evaluation on the decomposed modes and then select a mode signal representing the original signal to perform fault feature extraction, thereby achieving the purpose of fault diagnosis, improving the efficiency and having strong applicability.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a signal automatic decomposition method applied to wind turbine generator gearbox fault diagnosis is characterized by comprising the following steps:
step 1: acquiring a fault vibration signal f of a high-speed shaft gear of a gearbox, and defining the fault vibration signal f as a fault vibration signal x (i is 0) and f, wherein the initial decomposition frequency i of the fault vibration signal is 0;
step 2: VMD decomposing the fault vibration signal x (i) into a high frequency signal u (i)h(i) And a low frequency signal ul(i)；
And step 3: calculating a high frequency signal uh(i) And a low frequency signal ul(i) The dependency index p betweenh1(i)Degree of difference f from center frequencyd(i)；
And 4, step 4: based on the dependence index ρh1(i)Degree of difference f from center frequencyd(i) Determining a decomposition modality u (i);
and 5: after removing the decomposition mode u (i), the signal x (i +1) is updated to satisfy the following formula:
x(i+1)＝x(i)-u(i) (1)
wherein x (i +1) represents the updated fault vibration signal after the ith decomposition, x (i) represents the fault vibration signal before the ith decomposition,
step 6: the step 2 to the step 5 are circulated until the problems of under-decomposition and over-decomposition do not exist, and the iteration is terminated;
and 7: and outputting all decomposition modes [ u (1), u (2), … …, u (n) ] in the signal decomposition process to finish the automatic signal decomposition.
Further, in step 3, the dependence index ρh1(i)The formula of (1) is:
Figure BDA0003210710310000021
where i is the number of signal decompositions, m is the length of the modulus vector after decomposition, uh(i)(j) Represents the j value, u, in the high-frequency modal vector after the i-th decompositionl(i)(j) Represents the jth value in the low-frequency modal vector after the ith decomposition, j is 1,2h1(i)Is a high-frequency signal uh(i) And a low frequency signal ul(i) The dependency index between the two signals is used for measuring the high-frequency signal uh(i) And a low frequency signal ul(i) Dependence between, ph1(i)Is in the range of [ -1,1]，
Figure BDA0003210710310000031
Representing a high frequency signal uh(i) Is determined by the average value of (a) of (b),
Figure BDA0003210710310000032
representing a low frequency signal ul(i) Average value of (a).
Further, the calculation formula of the center frequency difference in step 3 is:
fd(i)＝(fh(i)-fl(i))/fl(i) (3)
wherein f isd(i) The central frequency difference degree between the high-frequency signal and the low-frequency signal after the ith decomposition is represented, and the larger the coefficient is, the larger the central frequency difference between the two signals is, the larger the difference of the signals is; the smaller the coefficient is, the closer the center frequencies of the two signals are, the higher the signal similarity is, fh(i) Representing the center frequency, f, of the high-frequency signal after the i-th decompositionl(i) Representing the center frequency of the low frequency signal after the i-th decomposition.
Further, the decision criterion of step 4 is:
(ii) when dependent on the exponent ρhl(i)Is not less than rho s and the central frequency difference degree fd(i) When fs is more than or equal to fs, the decomposition effect is better,
there is no under-and over-decomposition problem, when the decomposition mode u (n) satisfies the following formula:
u(n)＝uh(n)+ul(n) (4)
wherein n represents the number of signal decompositions when iteration is terminated, u (n) represents the decomposition mode when decomposition is stopped, and u (n) represents the decomposition mode when decomposition is stoppedh(n) high frequency signal of nth decomposition, u1(n) the low frequency signal of the nth decomposition;
in other cases, the decomposition mode u (i) is the result of low frequency signals, that is, the following formula is satisfied:
u(i)＝ul(i) (5)
ρ s and fs are a dependency exponent threshold and a center frequency difference threshold, respectively.
Further preferably, after the signal is automatically decomposed into a plurality of modalities in step 7, in order to improve the efficiency of the fault feature extraction, feature extraction is performed on one modality having the most fault information among the decomposed modalities.
Preferably, the selection of the mode is performed by a kurtosis formula of the fault information.
The kurtosis formula of the fault information is as follows:
Figure BDA0003210710310000033
wherein u (k) is the i-th decomposition mode, i is 1,2, … …, n (n is the decomposition times), u (i) (j) represents the j-th value in the i-th decomposition mode,
Figure BDA0003210710310000034
represents the expected value of the mode u (i) of the i-th decomposition, m is the length of the decomposed mode vector u (i), and j is 1,2.
Cu (i) represents the kurtosis value of u (i), Cu (i) is not less than k, which shows that the more impact components in the fault vibration signal are, the more serious the fault degree is, so the largest Cu (i) is selected as the mode containing the most fault information.
Wherein k is 3.
The invention has the beneficial effects that:
aiming at the problems in the prior art, the invention provides a signal automatic decomposition method applied to the wind turbine generator gearbox fault diagnosis, which can achieve the effect of automatic decomposition of signals by depending on indexes and central frequency difference degrees, and can perform multi-dimensional evaluation on the decomposed modes and then select a mode signal representing the original signal to perform fault feature extraction, thereby achieving the purpose of fault diagnosis, improving the efficiency and having strong applicability.
1. The invention creatively provides that parameters in the VMD algorithm are improved by taking the central frequency difference degree and the dependency index as the judgment criteria, the problem that the determination of the mode number K parameter in the VMD algorithm is not guided by scientific theory is solved, the effect of automatically decomposing signals is realized, and the algorithm parameters are not required to be set every time.
2. The decision criterion of signal decomposition designed by the invention avoids the problems of signal under-decomposition and over-decomposition caused by uncertain parameters of the algorithm, and provides scientific basis for the degree of signal decomposition.
3. When each decomposed mode is analyzed, each mode is evaluated by adopting a kurtosis formula of fault information, and the mode with the highest kurtosis is selected for envelope spectrum analysis, so that the noise reduction and filtering effects in the signal processing process of the algorithm are exerted to the maximum extent, the signal-to-noise ratio of the signal is greatly enhanced, and the final characteristic extraction result is obviously verified.
Drawings
FIG. 1 is a flow chart of the improved VMD automatic decomposition designed by the present invention;
FIG. 2 is a flow of fault feature extraction according to the present invention;
FIG. 3 illustrates the modal components after the auto-decomposition of the present invention;
FIG. 4 shows the frequency spectrum of each mode of the present invention;
fig. 5 shows the envelope spectrum before and after the automatic decomposition of the signal according to the invention.
Detailed Description
VMD is an adaptive signal decomposition method that can decompose a nonlinear signal into several modes with different characteristic scales, each carrying different center frequencies and bandwidths. The core idea is a variational problem, the solution of the variational problem changes the constraint variational problem into an unconstrained variational problem by introducing a secondary penalty factor and a Lagrange multiplier, and finally, the problem is solved by adopting a multiplier alternating direction method. Compared with other signal decomposition methods, the VMD can effectively avoid the problem of mode aliasing in mode decomposition, has a solid theoretical foundation, can lay a foundation for signal feature extraction, and has more fault diagnosis researches.
In the VMD decomposition process, the K value of the signal decomposition modal parameter needs to be artificially determined, and the over-decomposition of the signal is caused by the over-set K value, so that the calculation time is greatly consumed, and the calculation cost is increased; and the K value is set to be too small, so that the signal is under-decomposed, the signal is not completely decomposed and contains some noise components, the signal-to-noise ratio of the signal cannot be effectively improved, and the later-stage feature extraction effect is not good. In the face of the problem that the K value setting lacks effective theoretical support, the invention utilizes the center frequency difference and the dependency coefficient among the decomposed modes to measure the signal decomposition effect and judge whether to carry out the next decomposition.
The following describes in detail a signal automatic decomposition method applied to the fault diagnosis of the wind turbine generator gearbox according to the present invention with reference to the accompanying drawings and specific embodiments.
The present invention will be described in further detail with reference to the following embodiments, which are illustrative only and not limiting, and the scope of the present invention is not limited thereby.
Example 1
As shown in fig. 1 and 2, a method for automatically decomposing a signal applied to fault diagnosis of a gearbox of a wind turbine generator is characterized by comprising the following steps:
step 1: acquiring a fault vibration signal f of a high-speed shaft gear of a gearbox, and defining the fault vibration signal f as a fault vibration signal x (i is 0) and f, wherein the initial decomposition frequency i of the fault vibration signal is 0;
step 2: VMD decomposing the fault vibration signal x (i) into a high frequency signal u (i)h(i) And a low frequency signal ul(i)；
And step 3: calculating a high frequency signal uh(i) And a low frequency signal ul(i) The dependency index p betweenh1(i)Degree of difference f from center frequencyd(i)；
And 4, step 4: based on the dependence index phl(i)Degree of difference f from center frequencyd(i) Determining a decomposition modality u (i);
and 5: after removing the decomposition mode u (i), the signal x (i +1) is updated to satisfy the following formula:
x(i+1)＝x(i)-u(i) (1)
wherein x (i +1) represents the updated fault vibration signal after the ith decomposition, x (i) represents the fault vibration signal before the ith decomposition,
step 6: the step 2 to the step 5 are circulated until the problems of under-decomposition and over-decomposition do not exist, and the iteration is terminated;
and 7: and outputting all decomposition modes [ u (1), u (2), … …, u (n) ] in the signal decomposition process to finish the automatic signal decomposition.
In the step 2, the number parameter k of the signal decomposition modes in the VMD is set to be 2, and the penalty factor parameter alpha is set to be 2000.
Further, in step 3, the dependence index ρh1(i)The formula of (1) is:
Figure BDA0003210710310000061
where i is the number of signal decompositions, m is the length of the modulus vector after decomposition, uh(i)(j) Represents the j value, u, in the high-frequency modal vector after the i-th decompositionl(i)(j) Represents the jth value in the low-frequency modal vector after the ith decomposition, j is 1,2h1(i)Is a high-frequency signal uh(i) And a low frequency signal ul(i) The dependency index between the two signals is used for measuring the high-frequency signal uh(i) And a low frequency signal ul(i) Dependence between, ph1(i)Is in the range of [ -1,1]，
Figure BDA0003210710310000062
Representing a high frequency signal uh(i) Is determined by the average value of (a) of (b),
Figure BDA0003210710310000063
representing a low frequency signal ul(i) Average value of (a).
Further, the calculation formula of the center frequency difference in step 3 is:
fd(i)＝(fh(i)-fl(i))/fl(i) (3)
wherein f isd(i) The central frequency difference degree between the high-frequency signal and the low-frequency signal after the ith decomposition is represented, and the larger the coefficient is, the larger the central frequency difference between the two signals is, the larger the difference of the signals is; the smaller the coefficient is, the closer the center frequencies of the two signals are, the higher the signal similarity is, fh(i) Representing the center frequency, f, of the high-frequency signal after the i-th decomposition1(i) Representing the center frequency of the low frequency signal after the i-th decomposition.
Further, the decision criterion of step 4 is:
(ii) when dependent on the exponent ρh1(i)Is not less than rho s and the central frequency difference degree fd(i) When fs is larger than or equal to fs, the decomposition effect is better, and the problems of under-decomposition and over-decomposition do not exist, and the decomposition mode u (n) meets the following formula:
u(n)＝uh(n)+ul(n) (4)
wherein n represents the number of signal decompositions when iteration is terminated, u (n) represents the decomposition mode when decomposition is stopped, and u (n) represents the decomposition mode when decomposition is stoppedh(n) high frequency signal of nth decomposition, u1(n) the low frequency signal of the nth decomposition;
in other cases, the decomposition mode u (i) is the result of low frequency signals, that is, the following formula is satisfied:
u(i)＝ul(i) (5)
ρ s and fs are respectively a dependency index threshold and a central frequency difference threshold, and in the case of the patent, according to multiple tests, an empirical value ρ s obtained by multiple iterations is 0.02 and fs is 0.4.
The invention aims to solve the problem that the K value is uncertain in the VMD decomposition process, initializing K to be 2, namely decomposing each signal into a high-frequency signal and a low-frequency signal after VMD, calculating the central frequency difference and the dependence index of two components after decomposition to measure the decomposition effect after each decomposition, and setting a threshold criterion for the two coefficients to judge whether to carry out next decomposition. By the method designed by the patent, the signal can achieve an automatic decomposition effect, and the problems of under-decomposition and over-decomposition of the signal are solved in the decomposition process.
In the improved VMD signal decomposition method, a gearbox high-speed shaft fault vibration signal is taken as an example, each modal information after signal decomposition is as shown in fig. 3, and the signal is automatically decomposed into 5 modal components. The spectral diagram of each modal component is obtained after fft is performed on the decomposed modal components, as shown in fig. 4, it can be seen from the spectral diagram that the center frequencies of each modal component after decomposition are sequentially arranged from high frequency to low frequency, each center frequency has no aliasing phenomenon, the frequency spectrum components of each mode are different, and a large number of overlapped frequency components are not present in general.
Example 2
When each decomposed mode is analyzed, each mode is evaluated by adopting a kurtosis formula of fault information, and the mode with the highest kurtosis is selected for envelope spectrum analysis, so that the noise reduction and filtering effects in the signal processing process of the algorithm are exerted to the maximum extent, the signal-to-noise ratio of the signal is greatly enhanced, and the final characteristic extraction result is obviously verified.
As shown in fig. 2, after the signal is automatically decomposed into a plurality of modalities in step 7, in order to improve the efficiency of fault feature extraction, feature extraction is performed on one modality having the most fault information among the decomposed modalities.
Preferably, the selection of the mode is performed by a kurtosis formula of the fault information.
The kurtosis formula of the fault information is as follows:
Figure BDA0003210710310000071
wherein u (i) is the i-th decomposition mode, i is 1,2, … …, n (n is the decomposition times), u (i) (j) represents the j-th value in the i-th decomposition mode,
Figure BDA0003210710310000072
represents an expected value of a mode u (i) representing the i-th decomposition, m is the length of a mode vector u (i) after the decomposition, and j is 1,2.
Cu (i) represents the kurtosis value of u (i), Cu (i) is not less than k, which shows that the more impact components in the fault vibration signal are, the more serious the fault degree is, so the largest Cu (i) is selected as the mode containing the most fault information.
Wherein k is 3.
In this experiment, the vibration was soAfter the barrier vibration signal is automatically decomposed by the improved VMD, the kurtosis of each decomposed mode is calculated, the kurtosis value of each mode is shown as the following table, and in order to improve the effect of fault feature extraction, the mode component u with the largest kurtosis value is selected1And feature extraction is carried out, the fault information contained in the modal component is most abundant, the modal component can represent the fault information in the original vibration signal most, and the signal-to-noise ratio is greatly enhanced compared with the original signal.
TABLE 1 kurtosis values for respective modal components
Figure BDA0003210710310000073
Figure BDA0003210710310000081
The envelope analysis is performed on u (1), and simultaneously the envelope analysis is performed on the original signal, and the obtained envelope spectrogram is shown in fig. 5. The original signal is not processed by the improved VMD, and the obtained envelope spectrum contains a large number of frequency components irrelevant to the fault characteristic frequency, which indicates that the original signal contains a large number of noises and the signal-to-noise ratio is not high, so that the fault of the gearbox is difficult to directly analyze in the envelope spectrum, and the fault diagnosis and analysis of the gearbox cannot be directly and accurately performed. After the improved VMD is automatically decomposed, the mode with the highest kurtosis is selected by adopting a kurtosis value method to carry out envelope analysis, the fault characteristic frequency, the second frequency multiplication, the third frequency multiplication and the like of the vibration signal of the gearbox are obviously demodulated in a final envelope spectrum, the fault characteristic frequency components are obviously prominent, the amplitude of components irrelevant to the high frequency of the fault special diagnosis is small, and the components irrelevant to the fault characteristic are filtered by the method.
While the present invention has been described in detail and with reference to the embodiments thereof as illustrated in the accompanying drawings, it will be apparent to one skilled in the art that various changes and modifications can be made therein. Therefore, certain details of the embodiments are not to be interpreted as limiting, and the scope of the invention is to be determined by the appended claims.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that many variations and modifications are possible within the scope of the invention as described herein, and that other embodiments are contemplated. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.

Claims (8)

1. A signal automatic decomposition method applied to wind turbine generator gearbox fault diagnosis is characterized by comprising the following steps:
step 1: acquiring a fault vibration signal f of a high-speed shaft gear of a gearbox, and defining the fault vibration signal f as a fault vibration signal x (i is 0) and f, wherein the initial decomposition frequency i of the fault vibration signal is 0;
step 2: VMD decomposing the fault vibration signal x (i) into a high frequencySignal uh(i) And a low frequency signal ul(i)；
And step 3: calculating a high frequency signal uh(i) And a low frequency signal ul(i) The dependency index p betweenhlρhl(i)Degree of difference f from center frequencyd(i)；
And 4, step 4: based on the dependence index ρhl(i)Degree of difference f from center frequencyd(i) Determining a decomposition modality u (i);
and 5: after removing the decomposition mode u (i), the signal x (i +1) is updated to satisfy the following formula:
x(i+1)＝x(i)-u(i) (1)
wherein x (i +1) represents the updated fault vibration signal after the ith decomposition, x (i) represents the fault vibration signal before the ith decomposition,
step 6: the step 2 to the step 5 are circulated until the problems of under-decomposition and over-decomposition do not exist, and the iteration is terminated;
and 7: and outputting all decomposition modes [ u (1), u (2), … …, u (n) ] in the signal decomposition process to finish the automatic signal decomposition.
2. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to the claim 1 is characterized in that: dependence index ρ in step 3hl(i)The formula of (1) is:
Figure FDA0003210710300000011
where i is the number of signal decompositions, m is the length of the modulus vector after decomposition, uh(i)(j) Represents the j value, u, in the high-frequency modal vector after the i-th decompositionl(i)(j) Represents the jth value in the low-frequency modal vector after the ith decomposition, j is 1,2hl(i)Is a high-frequency signal uh(i) And a low frequency signal ul(i) The dependency index between the two signals is used for measuring the high-frequency signal uh(i) And a low frequency signal ul(i) Dependence between, phl(i)Is in the range of [ -1,1]，
Figure FDA0003210710300000012
Representing a high frequency signal uh(i) Is determined by the average value of (a) of (b),
Figure FDA0003210710300000013
representing a low frequency signal ul(i) Average value of (a).
3. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to claim 2, wherein the calculation formula of the central frequency difference in the step 3 is as follows:
fd(i)＝(fh(i)-fl(i))/fl(i) (3)
wherein f isd(i) The central frequency difference degree between the high-frequency signal and the low-frequency signal after the ith decomposition is represented, and the larger the coefficient is, the larger the central frequency difference between the two signals is, the larger the difference of the signals is; the smaller the coefficient is, the closer the center frequencies of the two signals are, the higher the signal similarity is, fh(i) Representing the center frequency, f, of the high-frequency signal after the i-th decompositionl(i) Representing the center frequency of the low frequency signal after the i-th decomposition.
4. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to the claim 1 is characterized in that: the decision criterion of step 4 is:
(ii) when dependent on the exponent ρhl(i)Is not less than rho s and the central frequency difference degree fd(i) When fs is larger than or equal to fs, the decomposition effect is better, and the problems of under-decomposition and over-decomposition do not exist, and the decomposition mode u (n) meets the following formula:
u(n)＝uh(n)+ul(n) (4)
wherein n represents the number of signal decompositions at the end of the iteration, u (n) represents the decomposition mode when the decomposition stopsh(n) high frequency signal of nth decomposition, ul(n) the low frequency signal of the nth decomposition;
in other cases, the decomposition mode u (i) is the result of low frequency signals, that is, the following formula is satisfied:
u(i)＝ul(i) (5)
ρ s and fs are a dependency exponent threshold and a center frequency difference threshold, respectively.
5. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to the claim 1 is characterized in that: after the signal is automatically decomposed into a plurality of modes in step 7, in order to improve the efficiency of fault feature extraction, feature extraction is performed on one mode containing the most fault information in the decomposed mode selection.
6. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to claim 5, wherein the selection of the mode is performed through a kurtosis formula of fault information.
7. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to claim 6, wherein the kurtosis formula of the fault information is as follows:
Figure FDA0003210710300000021
wherein u (i) is the i-th decomposition mode, i is 1,2, … …, n, n is the decomposition times, u (i) (j) represents the j-th value in the i-th decomposition mode,
Figure FDA0003210710300000022
representing an expected value of a mode u (i) representing the i-th decomposition, m being the length of a decomposed mode vector u (i), j being 1,2.. m;
cu (i) represents the kurtosis value of u (i), Cu (i) is not less than k, which shows that the more impact components in the fault vibration signal are, the more serious the fault degree is, so the largest Cu (i) is selected as the mode containing the most fault information.
8. The method for automatically decomposing the signal applied to the fault diagnosis of the gearbox of the wind turbine generator set according to claim 7, wherein k is 3.
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