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

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.