A Wind Turbine Fault Diagnosis Method Using Adaptive Weighting Algorithm and D-S Evidence Theory

2018 ◽  
Author(s):  
Xianhao Shen ◽  
Kangyong Liu ◽  
Zhu Liu ◽  
Miao Ye
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Evidence Theory ◽  
Adaptive Weighting ◽  
Diagnosis Method

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.

scholarly journals A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Zitao Zheng ◽  
Bing Qi ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Evidence Theory ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Diagnosis Method ◽  
Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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.

Sign in / Sign up

Export Citation Format

Share Document