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Shock and Vibration
Volume 2019, Article ID 7490750, 14 pages
https://doi.org/10.1155/2019/7490750
Research Article

A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion

1School of Electric Power, South China University of Technology, Guangzhou 510640, China
2Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou 510640, China
3School of Automation, Guangdong University of Technology, Guangzhou 510006, China
4Hunan New Energy Development Co.,Ltd., Guodian Power, Changsha 410016, China

Correspondence should be addressed to Zhuoli Zhao; moc.liamg@tucsilouhz

Received 3 December 2018; Revised 19 January 2019; Accepted 19 February 2019; Published 14 March 2019

Academic Editor: Enrico Zappino

Copyright © 2019 Xiafei Long et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. Then, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. The dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. The effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances.