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 Direct...
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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.
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