Robust bearing fault diagnosis under strong impulsive interference via envelope spectrum harmonic-to-noise ratio guided sparse filtering

Abstract

Early fault signatures of rolling bearings are typically very weak and are often contaminated by strong background noise and random impulsive interference. Traditional kurtosis-based feature extraction methods are highly sensitive to non-fault-related large random impacts, which may result in incorrect frequency-band selection. To address this issue, an improved sparse filtering (SF) method is proposed in this paper. First, to overcome the unstable convergence caused by random initialization in conventional SF, a linear uniform initialization strategy is introduced, in which a uniformly distributed band-pass filter bank is constructed as the initial weight matrix to ensure full-band feature extraction. Second, the envelope spectrum harmonic-to-noise ratio (ESHNR) is employed as an evaluation criterion to adaptively select the optimal filter containing periodic fault characteristics from multiple filters generated during iteration, thereby effectively suppressing random interference. The key novelty of this method is that the fault-characteristic frequency and its harmonic structure are incorporated into the sparse-filtering-based component selection process, enabling the selected component to reflect fault-related periodicity rather than merely high impulsiveness. The effectiveness of the proposed method is verified using simulation signals and experimental inner- and outer-race bearing fault signals under strong random impulsive interference.