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A Two-Step Denoising Strategy for Early-Stage Fault Diagnosis of Rolling Bearings

Empirical wavelet transform (EWT) is an adaptive tool for vibration signals processing and has been adopted for fault diagnosis of rolling bearings, while still suffers two weaknesses in detecting the weak, early-stage defects. First, adaptive and robust boundaries determination of the EWT segments is a challenge. Second, vestigial noise and unwanted vibrations still exist in the filtered signal, which may bury the weak fault signature and weaken the detection performance of early-stage faults. In this article, a two-step denoising strategy (TSDS) is proposed for early-stage fault detection, which includes frequency filtering and time-domain denoising steps. In the scheme, an automated EWT segmentation method is proposed for identifying each primary harmonic component; then, EWT-based correlated kurtogram is adopted to optimize the filtering frequency band such that to enhance the signal-to-noise ratio (SNR) of the fault information. Furthermore, the two-layer sliding-correlated kurtosis (TLSCK) algorithm is applied as the second step, which eliminates the vestigial noise and outputs pure periodic pulses, indicating the occurrence moments of the fault impulses. Thus, the weak, early-stage fault signature can be significantly enhanced and detected. A simulation study is conducted and validates the successfulness of the proposed scheme when the SNR is as low as −19 dB. A run-to-failure experiment verifies the effectiveness of the proposed method for weak faults identification, and comparison studies show that the proposed scheme could diagnosis bearing defects at much earlier moment than traditional methods like spectral kurtosis and dyadic wavelet transform.