Maximum reweighted local kurtosis deconvolutional filtering and application for time-varying speed rotating machinery fault diagnosis

Abstract

Blind deconvolution is a widely studied vibration analysis tool for extracting fault-related information from vibration signals. However, existing mainstream blind deconvolution methods struggle to extract fault information from strongly random, noisy, and non-smooth vibration signals. To address this issue, this study proposes a novel blind deconvolution method, named maximum reweighted local kurtosis deconvolution (MRLKD), which effectively extracts fault pulses from vibration signals under time-varying speed conditions. First, a novel evaluation metric, reweighted local kurtosis (RLK), is introduced, which exhibits strong robustness against outliers and noise while remaining unaffected by rotational speed variations. Second, the optimal filter is iteratively computed by maximizing the RLK objective function, enabling the extraction of transient pulses from the time-domain signal. Finally, the filtered signals are resampled from the time domain to the angular domain, and fault features are extracted through envelope order spectrum analysis, thereby facilitating fault diagnosis under time-varying speed conditions. The effectiveness and reliability of the proposed method are verified through simulations and experimental signals, compared with existing techniques. The results demonstrate the advantages of the proposed method.