An interpretable algorithm unrolling network inspired by group-based sparse coding for mechanical fault diagnosis

Abstract

Intelligent fault diagnosis (IFD) can provide reliable maintenance decision-making for mechanical systems. Although generic deep learning models have been applied successfully in IFD, most are purely data-driven and lack interpretability in architectures and features. To this end, an interpretable IFD model is proposed by using algorithm unrolling from a signal processing perspective. For a group-based sparse coding (GSC) denoising problem, we first propose a new denoising algorithm called EGISTA, which combines the extragradient and the group-based iterative shrinkage-thresholding algorithm (EGISTA). Then, an algorithm unrolling network called EGISTA-Net is derived from EGISTA. EGISTA-Net has an interpretable encoder–decoder architecture since the architecture is constructed under the guidance of the GSC denoising algorithm. We apply EGISTA-Net to two IFD tasks. In ablation studies, we show that the extragradient can speed up EGISTA-Net’s convergence and improve noise robustness, while the decoder can enhance the interpretability of extracted features. We also interpret the feature extraction process of EGISTA-Net by visualizing the signals reconstructed using layerwise features. Compared with state-of-the-art IFD models, EGISTA-Net achieves higher diagnostic accuracy with limited training set sizes and shows stronger noise robustness. Our experiment results demonstrate that EGISTA-Net is a promising interpretable IFD model.