Gabor Wavelet Initialized Sparse Filtering: An Interpretable Network for Bearing Fault Diagnosis

Gabor wavelet has excellent properties in extracting local spatial and frequency domain information of a target. While it cannot inherently constitute an orthogonal basis, it can form a compact frame under specific parameters, which provides the possibility of using Gabor wavelets for feature extraction. In this paper, a novel Gabor wavelet initialized sparse filtering (GWISF) is designed to bolster the interpretability and efficacy of intelligent networks for bearing fault diagnosis. Specifically, a set of Gabor wavelets are proposed to initialize the weight vectors of the SF to extract multi-scale and multi-directional fault features, and SE attention is used to guide the training of network weights. This approach aims to improve fault detection accuracy and computational efficiency. Two bearing datasets under different working conditions are employed to demonstrate the efficacy of the GWISF model. The experimental outcomes reveal that the proposed GWISF method outperforms other methods, exhibiting the highest diagnostic accuracy and robustness.