Fault detection of planetary subassemblies in a wind turbine gearbox using TQWT based sparse representation

Planetary subassemblies in wind turbine gearbox are subject to compound faults due to harsh environment and complex structure. Disturbed by the meshing vibration from higher-speed transmission stage and intensive noise, fault diagnosis of planetary subassemblies with low rotational speed is challenging. In this paper, a tunable Q-factor wavelet transform based sparse representation method is proposed, which integrates the property of tunable Q wavelet transform, non-convex penalty and noise optimization into sparse decomposition. This method makes it possible to accurately decompose vibration signal from faulty planetary subassemblies into two resonance components and noise, relying less on the setting of regularization parameters due to the noise restriction. It is easier to detect potential fault information in decomposed low or high resonance component than in the original signal. Further, normalized multi-stage enveloping spectrogram is presented to reveal the fault characteristic frequencies of planetary gears and bearings even though they are weak. The effectiveness of the proposed methods is verified by the analysis of a simulated faulty signal and an on-site case from one 850 kW wind turbine gearbox. (C) 2020 Elsevier Ltd. All rights reserved.