Towards Trustworthy Degradation Prediction: An Interpretable Deep Learning Approach with Sparse Feature Extraction and Temporal Fusion

Degradation prediction of industrial equipment is crucial for ensuring the reduction of downtime and optimizing maintenance strategies. Although the existing Deep Learning (DL) based estimation methods provide accurate predictions with generalizability, the interpretable extraction of deep features related to degradation has not been discussed. It is also challenging to interpret and trace the temporal dynamics of deep features, including regular degradation accumulation and abnormal situations. To address these issues, this paper proposes an interpretable and traceable framework for degradation prediction. First, the Degradation-Informed Interpretable Encoder (DIIE) encodes the raw signal into sparse features, in which the parametric wavelet kernel and degradation constraint are designed to guide the automatic degradation feature extraction. Then the Interpretable Temporal Fusion Module (ITFM) with binarized gating values is used to directly process the multi-step features with more transparency. Finally, the temporal-enhanced features are fed into the predictor to make inferences. The proposed approach was validated on a bearing degradation dataset and has achieved competitive predictive performance. Additionally, it provides interpretations for feature extraction and temporal fusion, which can improve the understanding and trustworthiness regarding the prediction of mechanical degradation.