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.

scholarly journals Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2339 ◽  
Author(s):  
Aijun Yin ◽  
Yinghua Yan ◽  
Zhiyu Zhang ◽  
Chuan Li ◽  
René-Vinicio Sánchez
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Short Term Memory ◽  
Wind Turbine Gearbox ◽  
Vibration Data ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Lstm Network ◽  
Wavelet Energy Entropy ◽  
Improve Cost

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.

scholarly journals Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

2021 ◽  
Author(s):  
Wei Dong ◽  
shuqing zhang ◽  
Mengfei Hu ◽  
Liguo Zhang ◽  
Haitao Liu
Keyword(s):  
Pattern Recognition ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Feature Reduction ◽  
Maintenance Cost ◽  
Joint Entropy ◽  
Wind Turbine Gearbox ◽  
Multi Scale ◽  
Diagnosis Method

Abstract The fault diagnosis of gearbox and bearing in wind turbine is crucial to improve service life and reduce maintenance cost. This paper proposes a novel fault diagnosis method based on refined generalized composite multi-scale state joint entropy (RGCMSJE), robust spectral learning framework for unsupervised feature selection (RSFS) and extreme learning machine (ELM) to identify the different health conditions of gearboxes, including feature extraction, feature reduction and pattern recognition. In this method, MAED is firstly adopted to assist RGCMSJE in parameter selection. Second, RGCMSJE is utilized to extract the multi-scale features of gearbox vibration signal and construct high-dimension feature set. Thirdly, RSFS method is used to reduce the dimension of high-dimensional RGCMSJE feature set. In the end, the obtained low-dimensional features are input to the ELM classifier to realize fault pattern recognition. Through two gearbox fault diagnosis experiments, the effectiveness of the fault diagnosis method is verified. The analysis results show that this method can effectively and accurately identify different fault types of 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.

Fault Diagnosis of Wind Turbine Gearbox Based on Neighborhood QPSO and Improved D-S Evidence Theory

2020 ◽  
Vol 13 (2) ◽  
pp. 248-255
Author(s):  
Jiatang Cheng ◽  
Yan Xiong ◽  
Li Ai
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
High Reliability ◽  
Evidence Theory ◽  
Population Diversity ◽  
Weight Of Evidence ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method ◽  
Drive Chain ◽  
The Individual

Background: Gearbox is the key equipment of wind turbine drive chain. Due to the harsh operating environment of wind turbine, gearbox failures occur frequently. Methods: To improve the accuracy of fault identification for wind turbine gearbox, an intelligent fault diagnosis method based on Neighborhood Quantum Particle Swarm Optimization (NQSPO) and improved Dempster-Shafer (D-S) evidence theory is proposed. In NQPSO algorithm, the best solution information in the neighborhood is introduced to guide the individual search behavior and enhance the population diversity. Also, the consistency coefficient is used to determine the weight of evidence, and the original evidence is amended to enhance the ability of D-S theory to fuse conflict evidence. Results: Experimental results show that the proposed method can overcome the influence of bad evidence on the diagnosis result and has high reliability. Conclusion: The research can effectively improve the accuracy of fault diagnosis of wind turbine gearbox, and provide a feasible idea for the fault diagnosis of nonlinear complex system.

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.

Sign in / Sign up

Export Citation Format

Share Document