Wind Turbine Gearbox Fault Diagnosis Based on Wavelet Theory and Hilbert Demodulation Spectrum

2014 ◽  
Vol 703 ◽  
pp. 390-393 ◽  
Author(s):  
Hui Wang ◽  
Gui Ge Gao ◽  
Xian Wen Zeng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Fault Simulation ◽  
Vibration Signal ◽  
Wavelet Theory ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method ◽  
The Hilbert Transform

Through the mechanism of the gearbox’s vibration signal and establish the corresponding mathematical model, then establish a fault diagnosis method based on the wavelet theory and Hilbert demodulation spectrum. First, the wavelet threshold de-noising can be used to reducing noise of the gearbox’s vibration signal. Then, use the wavelet packet decomposition to decomposing the de-noising signal into different frequency band. After that, use the Hilbert transform to demodulate the frequency band that focused power. Finally, extract the fault characteristic value for the fault diagnosis. Through a fault simulation vibration signal test the method, the results show that the method can effectively extract the fault information of the wind turbine gearbox.

scholarly journals Fault diagnosis of wind turbine gearbox based on wavelet neural network

2018 ◽  
Vol 37 (4) ◽  
pp. 977-986 ◽  
Author(s):  
Chen Huitao ◽  
Jing Shuangxi ◽  
Wang Xianhui ◽  
Wang Zhiyang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Network Model ◽  
Vibration Signal ◽  
Wavelet Neural Network ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method ◽  
Fault Mechanism ◽  
The Relationship

In order to monitor the wind turbine gearbox running state effectively, a fault diagnosis method of wind turbine gearbox is put forward based on wavelet neural network. Taking a 1.5 MW wind turbine gearbox as the target of study, the frequency spectrum of vibration signal and the fault mechanism of driving part are analyzed, and the eigenvalues of the frequency domain are extracted. A wavelet neural network model for fault diagnosis of wind turbine gearbox is established, and wavelet neural network is trained by using different feature vectors of fault types. The relationship between fault component and vibration signal is identified, and the vibration fault of wind turbine gearbox is predicted and diagnosed by network model. The analysis results show that the method can diagnose fault and fault pattern recognition of wind turbine gearbox very well.

scholarly journals Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shoubin Wang ◽  
Xiaogang Sun ◽  
Chengwei Li
Keyword(s):  
Time Series ◽  
Riemannian Manifold ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Covariance Matrix ◽  
Multivariate Time Series ◽  
Time Sequence ◽  
Mechanical Equipment ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method

As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.

Vibration-Based Fault Diagnosis of Pump Using WPT

2008 ◽  
Author(s):  
Pan Hong ◽  
Zheng Yuan
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Accurate Method ◽  
Singularity Analysis ◽  
Good Time ◽  
Signal Denoising ◽  
Good Tool ◽  
Time Frequency ◽  
Diagnosis Method

A vibration-based fault diagnosis method of pump units based on wavelet packet transform (WPT) is proposed in this paper. Compared with Fourier transform (FT) and wavelet transform (WT), WPT can subdivide the whole time-frequency domain. It can perform signals with good time resolution at high frequency and vice versa. WPT is considered as a good tool to signal denoising, accounting for its perfect ability in decomposing and reconstructing signal and its characteristic of no redundancy and divulges after denoising. In addition, WPT modulus maximal coefficient provides a simple but accurate method in calculating the Lipschitz exponents, which is the measurement of signal singularity. According to the singularity analysis results of vibration signal, we can recognize the fault pattern of pump units. This paper makes a detail research on signal denoising and singularity analysis based on WPT. Taking the main shaft and thrust bearing vibration signal for example, the experimental results show that WPT is effectively in the fault diagnosis system of pump unit.

Research of Automobile Engine Fault Diagnosis Based on Wavelet Packet

2012 ◽  
Vol 472-475 ◽  
pp. 795-798
Author(s):  
Min Yong Tong
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Automobile Engine ◽  
Band Energy ◽  
Diagnosis Method ◽  
S Model ◽  
Frequency Band Energy

A diagnosis method using wavelet packet, frequency band energy analysis and neural network was presented for the automobile engine fault diagnosis. Fault signal of automobile engine was decomposed at different frequency band by wavelet packet. According to the change of frequency band energy, fault frequency band of the automobile engine was found. Fault diagnosis knowledge is described by means of applying T-S model. Results from the experimental signal analysis show that the proposed method is effective in diagnosing the automobile engine faults.

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

Research of Single Component Shock Methods in Wind Turbine Fault Diagnosis

2013 ◽  
Vol 805-806 ◽  
pp. 303-311
Author(s):  
Ning Jia ◽  
Tian Xia Zhang ◽  
Yuan Sheng Li ◽  
Tao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Farm ◽  
Vibration Signal ◽  
Single Component ◽  
Vibration Monitoring ◽  
Generator System ◽  
Diagnosis Method ◽  
Demodulation Method ◽  
Fault Characteristic

The structure of the wind turbine generator system is complex and it is difficult to identify the fault signals because of fault frequency aliasing on the vibration characteristics. The wind turbine fault diagnosis method is raised on single component shock to solve the vibration signal feature extraction during the wind turbines operating. Based on the principle of Hilbert envelope demodulation, this envelope demodulation method is presented for the single IMF component which contains shock fault characteristic frequency to solve the possible problem which fault Frequency is difficult to identify when the original signal is directly asked to envelope. This method has been applied and verified when a wind farm CSC-855W wind turbine vibration monitoring device was presented. The results show that compared with the traditional envelope demodulation method, by this method wind turbine fault characteristic can be more effectively and directly extracted and the accuracy of fault diagnosis can be improved. It is of great practical value.

Fault Diagnosis of Automobile Rear Axle Based on Wavelet Packet and Support Vector Machine

2011 ◽  
Vol 211-212 ◽  
pp. 1021-1026 ◽  
Author(s):  
Yong Chen ◽  
Bao Qiang Wang ◽  
Jin Yao
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Band ◽  
Rear Axle ◽  
Wavelet Packet ◽  
Support Vector ◽  
Svm Classifier ◽  
Failure State ◽  
Diagnosis Method ◽  
Specific Frequency

This paper presents a fault diagnosis method of automobile rear axle based on wavelet packet analysis (WPA) and support vector machine (SVM) classifier. By Fourier transformation we find out the frequency band that can mostly reflect the rear axle failure state and use wavelet packet to decompose and reconstruct the vibration signals of rear axle, then extract each band’s energy and the variance, standard deviation, skewness, kurtosis of the specific frequency band to constitute a feature vector. We use the feature vectors which are come from some pieces of normal and abnormal samples to train support vector machine classifier for obtaining the best classification，at the same time, discuss the optimization of SVM parameters. Application shows that the method is effective in real time fault diagnosis for the automobile rear axle and has a strong anti-interference ability in different working conditions.

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