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Wind turbine drive chain vibration noise suppression and incipient fault feature extraction method

A technology of vibration, noise, and early failure, applied in the field of condition monitoring of wind turbines, can solve problems such as difficulty in extracting characteristic frequencies, affecting early detection, failure warning and life management level of condition monitoring system, and difficulty in extracting early weak failure characteristics, etc.

Inactive Publication Date: 2014-12-10
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the randomness and uncertainty of wind speed and the control characteristics of variable speed and constant frequency generation of wind turbines, the vibration signal in the condition monitoring is easily interfered by various noises, which makes it difficult to extract the characteristic frequency, which directly affects the early stage of the condition monitoring system. Detection, fault warning and life management level; in addition, the analysis of existing wind turbine vibration status monitoring is mostly based on the independent analysis of the vibration signal of a single measuring point, and it is difficult to extract early weak fault features

Method used

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  • Wind turbine drive chain vibration noise suppression and incipient fault feature extraction method
  • Wind turbine drive chain vibration noise suppression and incipient fault feature extraction method
  • Wind turbine drive chain vibration noise suppression and incipient fault feature extraction method

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Experimental program
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Effect test

Embodiment 1

[0047] figure 1 It is an analysis process based on EMD-WPT supplemented by multi-measuring point data fusion, as shown in the figure: a method for suppressing vibration and noise of a wind turbine transmission chain and its early fault feature extraction method provided by the present invention includes the following steps:

[0048] 1) Extract fault feature spectrum vector X j . For the collected vibration raw data of J group, using the EMD-WPT characteristic frequency extraction method, the fault characteristic spectrum vector X is extracted j (x j1 , x j2 ,...,x jn ), where j=1, 2, ..., J; n is the dimension of the spectrum vector. And initialize j=1.

[0049] 2) Preprocessing of spectrum vector. First judge the value of j, if j≤J, the input signal X j , and the spectrum vector is processed as follows.

[0050] z pi = x ...

Embodiment 2

[0075] Now with image 3 The fault feature extraction of the original vibration signal of the front and rear bearings of the actual generator is shown as an example to specifically illustrate the method of the present invention:

[0076] Firstly, EMD is used to decompose the vibration signals of the front and rear bearings of the generator respectively, and 15 groups of IMFs are obtained. Here, the IMFs related to the fault characteristics are selected and accumulated. 5 ~IMF 14 Get the reconstructed signal S 2 (t), and then conduct autocorrelation analysis on it, and finally use the wavelet packet transform (WPT) feature frequency extraction method to obtain the following Figure 4 As shown in the spectrogram, the fault characteristic spectrum vector X is established j ; Then use the multi-measuring point data fusion method based on EMD-WPT to analyze X j Fusion analysis is carried out, and the fault characteristic spectrum after fusion is as follows Figure 5 shown.

...

Embodiment 3

[0079] Now, the method of the present invention will be specifically described by taking the analysis of vibration signals of the front and rear bearings of the simulated generator as an example. Among them, the vibration signal source considers the following components: the vibration signal source considers the following components: 100Hz and its double frequency of the generator front bearing fault component; 250Hz weak early fault component; 150Hz short-term interference noise component; Frequency band component; Gaussian white noise component that simulates environmental disturbances. The sampling frequency is 25600Hz, and the simulation time is set to 1s, that is, the number of sampling points is 25600. The specific simulated vibration signal is as follows:

[0080] 1) Signal 1:

[0081] S 1 (t)=0.2sin(2π×100t)+0.15sin(2π×200t)

[0082] 2) Signal 2:

[0083] S 2 (t)=0.06sin(2π×250t)

[0084] 3) Signal 3:

[0085] S 3 (t)=sin(2π×150t)e -20t

[0086] 4) Signal 4:...

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Abstract

The invention relates to a wind turbine drive chain vibration noise suppression and incipient fault feature extraction method. The method includes utilizing the experience mode to analyze single measuring point vibration signals, screening intrinsic mode functions capable of characterizing the fault feature frequency, reconstructing and acquiring fault feature signals to highlight the fault features, removing the noise effect on the constructed signals through autocorrelation analysis, combining with the wavelet packet transform feature frequency extraction method, and implementing the noise suppression of the single measuring point vibration signals; on the basis, utilizing the adaptive resonance theory to combine and analyze a plurality of measuring point vibration signal spectrum after the noise suppression treatment, and implementing the extraction of incipient fault features. The method has the advantages that the effects of background white noise and short interference noise can be removed effectively, the weak incipient fault feature frequency can be extracted, and the method can be directly applied to a wind turbine monitoring and fault diagnosis system and implement the diagnosis of wind turbine drive chain mechanical faults.

Description

technical field [0001] The invention belongs to the technical field of condition monitoring of wind turbines, and relates to a method for suppressing vibration and noise of a transmission chain of wind turbines and extracting early fault features. Background technique [0002] With the rapid development of my country's wind power industry and the grid-connected operation of large-scale wind power generation, research on how to apply wind turbine condition monitoring technology to reduce the frequency of wind turbine failures, operation and maintenance costs, and increase its power generation has attracted widespread attention. Among them, accurate and comprehensive extraction of fault characteristic signals and weak symptoms is the key to condition monitoring and fault diagnosis. Due to the randomness and uncertainty of wind speed and the control characteristics of variable speed and constant frequency generation of wind turbines, the vibration signal in the condition monito...

Claims

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Application Information

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IPC IPC(8): G01M13/02
Inventor 李辉杨东胡姚刚李洋杨超刘志祥梁媛媛欧阳海黎兰涌森
Owner CHONGQING UNIV
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