Affiliation:
1. The Engineering College, Honghe University, Mengzi 661199, China
Abstract
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.
Funder
National Natural Science Foundation of China
Scientific Research Fund of Yunnan Provincial Department of Education
Publisher
Bentham Science Publishers Ltd.
Reference30 articles.
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