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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

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

Author(s): Jiatang Cheng, Yan Xiong and Li Ai*

Volume 13, Issue 2, 2020

Page: [248 - 255] Pages: 8

DOI: 10.2174/2213275912666181218124805

Price: $65

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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.

Keywords: Wind turbine gearbox, fault diagnosis, neighborhood quantum particle swarm optimization, neural network, improved D-S evidence theory, evidence fusion.

Graphical Abstract

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