Sparse Impulse Feature Learning for Bearing Fault Extraction

Abstract

Bearing monitoring is typically based on signals acquired from accelerometers, where the operational status is inferred by analyzing potential signal features. In practical situations, fault features are often weak and obscured by background noise, which significantly increases the difficulty of weak feature extraction and condition diagnosis. Sparse representation (SR) has been widely adopted for feature extraction from vibration signals. However, classical algorithms generally exhibit limited robustness under low signal-to-noise ratio (SNR) conditions. To address this issue, a sparse impulse feature learning (SIFL) method is proposed in this article. First, a shift kurtosis spectrum (SKS) method is developed to automatically identify the potential number of impulse components and their initial center frequencies. Second, a composite convolutional constraint is constructed and incorporated into convolutional dictionary learning (CDL). Bandwidth and sparse nonconvex constraints are imposed simultaneously during optimization. Furthermore, an optimization strategy for the constrained bandwidth and center frequency is proposed. During iteration, SIFL adaptively updates the bandwidths and spectrum locations of different atoms to mine hidden features. Meanwhile, a differential envelope energy (DEE) is proposed to effectively evaluate the performance of different models. Compared with SR and its variants, SIFL achieves superior performance in both fault feature frequency identification and amplitude integrity preservation.