Time-frequency analysis via complementary ensemble adaptive local iterative filtering and enhanced maximum correlation kurtosis deconvolution for wind turbine fault diagnosis

A complementary ensemble adaptive local iterative filtering (CEALIF) and enhanced maximum correlation kurtosis deconvolution (EMCKD) approach is proposed for weak fault signals in wind turbine bearings, which are easily concealed by strong background noise and susceptible to intermittent interference. The adaptive local iterative filtering (ALIF), as a novel nonstationary signal processing technique, can perform adaptive filtering based on the signal itself characteristics. However, its mode mixing is an annoying problem. To relieve this problem, the noise-assisted CEALIF-based filtering is proposed. Nonetheless, the ambient noise present in the original signal is retained in the component of interest. Maximum correlation kurtosis deconvolution (MCKD) is an effective tool for enhancing periodic pulses. However, its deconvolution parameters need to be set manually, and are more demanding when the bearing failure is weak. To address this circumstance, this paper introduces the particle swarm algorithm (PSO) to solve the optimal deconvolution parameters and proposes EMCKD. Firstly, by employing CEALIF, the original signal is adaptively filtered into a sequence of IMFs. Then, the IMF that best characterizes the fault information is selected based on the weighted kurtosis index (WKI). Finally, the shock components of the selected IMF are enhanced to extract the periodic shock based on EMCKD. The proposed approach can accurately extract fault characteristics by analyzing the whole life cycle signals of bearings and fault signals of a 1.5 MW direct-drive wind turbine within strong background noise. Further, the proposed approach is implemented for the compound fault extraction of bearings, and the compound fault information of the inner race as well as the outer race of the bearings is successfully extracted. (C) 2021 The Author(s). Published by Elsevier Ltd.