Differentiated time-frequency envelope kurtosis-gram guided adaptive variational multi-harmonic mode extraction for fault diagnosis of offshore wind turbine slewing bearings

Abstract

Early fault characteristics of offshore wind turbine slewing bearings are weak and easily masked by noise, making accurate extraction difficult. To address this problem, a differentiated Time-Frequency Envelope Kurtosis-gram (TFEKgram) guided Adaptive Variational Multi-Harmonic Mode Extraction (AVMHME) strategy is proposed. The strategy mainly includes Optimized Frequency Band Component (OFBC) selection and fault characteristic extraction. First, the differentiated TFEKgram is developed for OFBC selection, in which a new Time-Frequency Envelope Kurtosis (TFEK) index is introduced to evaluate fault intensity. Dual Tree Complex Wavelet Packet Transform (DTCWPT) is then used to decompose the signal into sub-bands and construct the TFEKgram. The differentiated TFEKgram combines OFBC screening and fusion with a modulation strategy. OFBC screening and fusion separate compound fault characteristics, while the modulation strategy enhances fault characteristics. For fault characteristic extraction, AVMHME is developed by combining an Inverted Maximum Correlation Kurtosis Spectrum (IMCKS) for center frequency selection and a Penalty Factor Mapping Equation (PFME) for penalty factor determination. AVMHME extracts fault characteristics from the enhanced fused OFBC, enabling accurate diagnosis of slewing bearing fault types. Analyses of simulated and experimental signals verify the effectiveness of the proposed strategy, and comparative results show better fault characteristic extraction performance than conventional methods.