A Novel Interpretable Model via Algorithm Unrolling for Intelligent Fault Diagnosis of Machinery

Identifying the health state of machinery using sensor information is one of the key tasks for intelligent operation and maintenance of machinery. In recent years, deep learning models have been widely used in this field. However, these models usually lack interpretability. To this end, we use the algorithm unrolling technique to unroll the convolutional version of iterative shrinkage-thresholding algorithm (CISTA) into a neural network called CISTA-Net. The CISTA-Net’s structure is determined by the iterative algorithm and thus has a clear theoretical basis. By adding a classification module and a reconstruction module in parallel to its output end, the CISTA-Net is interpretable in terms of both its structure and output. Interpretability analysis through the well-known bearing dataset from the Case Western Reserve University (CWRU) shows that the reconstruction module can enhance the interpretability. In addition, a simple residual block can be optionally added to the input end of the CISTA-Net for feature pre-extraction, resulting in an alternative CISTA-Net called ResCISTA-Net. The ResCISTA-Net is compared with some conventional and interpretable fault diagnosis models on another two mechanical fault diagnosis tasks. Experimental results suggest that: 1) the residual block can not only improve diagnostic accuracy but also prevent performance degradation for some tasks; 2) the reconstruction module can also improve diagnostic accuracy; and 3) the ResCISTA-Net performs excellently in terms of diagnostic accuracy, training sample size requirement, and noise robustness.