Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM

2013 ◽  
Vol 50 ◽  
pp. 1-6 ◽  
Author(s):  
Liu Wenyi ◽  
Wang Zhenfeng ◽  
Han Jiguang ◽  
Wang Guangfeng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Binary Tree ◽  
Diagnosis Method

Fault Diagnosis Method for Wind Turbine Gearbox Based on Image Characteristics Extraction and Actual Value Negative Selection Algorithm

2020 ◽  
Vol 34 (14) ◽  
pp. 2054034
Author(s):  
Xiaoli Xu ◽  
Xiuli Liu
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Negative Selection ◽  
Selection Algorithm ◽  
Turbine Generator ◽  
Time Frequency ◽  
Negative Selection Algorithm ◽  
Wind Turbine Generator ◽  
Image Characteristics ◽  
Diagnosis Method

With the development of information theory and image analysis theory, the studies on fault diagnosis methods based on image processing have become a hot spot in the recent years in the field of fault diagnosis. The gearbox of wind turbine generator is a fault-prone subassembly. Its time frequency of vibration signals contains abundant status information, so this paper proposes a fault diagnosis method based on time-frequency image characteristic extraction and artificial immune algorithm. Firstly, obtain the time-frequency image using wavelet transform based on threshold denoising. Secondly, acquire time-frequency image characteristics by means of Hu invariant moment and correlation fusion gray-level co-occurrence matrix of characteristic value, thus, to extract the fault information of the gearing of wind turbine generator. Lastly, diagnose the fault type using the improved actual-value negative selection algorithm. The application of this method in the gear fault diagnosis on the test bed of wind turbine step-up gearbox proves that it is effective in the improvement of diagnosis accuracy.

A novel wind turbine bearing fault diagnosis method based on Integral Extension LMD

Measurement ◽  
2015 ◽  
Vol 74 ◽  
pp. 70-77 ◽  
Author(s):  
W.Y. Liu ◽  
Q.W. Gao ◽  
G. Ye ◽  
R. Ma ◽  
X.N. Lu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Integral Extension ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Turbine Bearing

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.

Research on Fault Diagnosis Method of Wind Turbine Based on Wavelet Analysis and LS-SVM

2013 ◽  
Vol 724-725 ◽  
pp. 593-597 ◽  
Author(s):  
Chang Liang Liu ◽  
Wei Xue Qi
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Wind Turbine ◽  
High Speed ◽  
Wavelet Decomposition ◽  
Least Square ◽  
Support Vector ◽  
Vibration Signals ◽  
Training Samples ◽  
Diagnosis Method

Aiming at the fault characteristics of high-speed gearbox fault diagnosis of wind turbine, a fault diagnosis method of combining wavelet analysis with least square-support vector machine (LS-SVM) is proposed. According to the method, the energy of frequency bands generated by wavelet decomposition and reconstruction of the high-speed gearbox's vibration signals in different fault states is normalized as eigenvectors, forming training and testing samples of LS-SVM fault classifier. Train the LS-SVM fault diagnosis model with the training samples and test the accuracy with the testing samples. The result of research shows that the fault diagnosis method based on the wavelet analysis and LS-SVM has good diagnostics effect.

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