A Fault Diagnosis Framework for Rolling Bearings Based on PPCA-AR Anti-Interference Preprocessing and LSTM

Abstract

Prevailing rolling bearing fault diagnosis frameworks based on long short-term memory (LSTM) are susceptible to noise interference under industrial strong-noise working conditions, suffering from insufficient feature extraction capability and low diagnostic precision. To address these limitations, this paper proposes a fault diagnosis framework integrating deep learning with signal processing, which consists of probabilistic principal component analysis (PPCA) for noise suppression, the autoregressive (AR) model for discrete interference elimination, spectral kurtosis (SK) for fault feature enhancement, and LSTM-based intelligent classification. To improve the signal-to-noise ratio (SNR) of vibration signals, the proposed method first estimates and suppresses noise via PPCA, and then eliminates periodic discrete frequency interferences represented by gear meshing components using the AR model. Following interference suppression, the SK method is adopted to implement multi-scale resonant frequency band screening and envelope demodulation. Finally, the demodulated features are learned by the LSTM to realize intelligent fault diagnosis of rolling bearings. This novel approach not only improves fault diagnosis accuracy but also enhances the model interpretability with the aid of signal processing techniques. Experimental results on the Case Western Reserve University (CWRU) and industrial field datasets demonstrate that the proposed method achieves superior accuracy compared with state-of-the-art approaches under various SNR conditions. It effectively mitigates the accuracy degradation of deep learning diagnostic models in strong-noise environments, providing a reliable technical solution for the intelligent diagnosis of rolling bearings.