An interpretable algorithm unrolling network inspired by general convolutional sparse coding for intelligent fault diagnosis of machinery

• Published in: Measurement : journal of the International Measurement Confederation Vol. 244; p. 116332
• Main Authors: Yuan, Menghan, Zeng, Ming, Rao, Fengpei, He, Zhiyi, Cheng, Yiwei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 28.02.2025
• Subjects: Algorithm unrolling; Alternating direction method of multipliers; Fault diagnosis; General convolutional sparse coding; Interpretable neural network; Signal denoising; Fault diagnosis; Alternating direction method of multipliers; Algorithm unrolling; Signal denoising; Interpretable neural network; General convolutional sparse coding
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.116332

•An interpretable model, GADMM-Net, is proposed for intelligent fault diagnosis (IFD).•The encoder-decoder architecture is derived from the denoising algorithm, ADMM.•GADMM-Net outperforms state-of-the-art interpretable models in multiple aspects.•GADMM-Net shows great potential in IFD tasks across different operating conditions. When applied in Intelligent Fault Diagnosis (IFD) of machinery, most of generic deep learning models lack interpretability in architecture design. From a signal processing perspective, we unroll the Alternating Direction Method of Multipliers (ADMM), the iterative algorithm for a general convolutional sparse coding denoising problem, into a deep neural network called the General ADMM Network (GADMM-Net) using algorithm unrolling. GADMM-Net has an encoder-decoder architecture where the encoder extracts signal features (i.e., sparse coefficients) and the decoder reconstructs denoised signals. As the encoder-decoder architecture is derived from the denoising algorithm, GADMM-Net is interpretable in its backbone architecture and inherits the prior domain knowledge behind the signal denoising problem. Compared with conventional and state-of-the-art IFD models, GADMM-Net performs more excellently in diagnostic accuracy, number of parameters, training set size requirement, and noise robustness. In the IFD task across different operating conditions, GADMM-Net shows domain generalization capabilities, without using any transfer learning strategy.