Decoupled Feature-Temporal CNN: Explaining Deep Learning-Based Machine Health Monitoring

Abstract

Machine learning, especially deep learning, has been extensively applied and studied in the area of machine health monitoring. For machine health monitoring systems (MHMS), major efforts have been put into designing and deploying more and more complex machine learning models. Those black-box models are nontransparent toward their working mechanism. However, this research trend brings huge potential risks in real life. Since machine health monitoring itself belongs to high stake decision applications, the outputs of the autonomous monitoring systems should be trustworthy and reliable, which refers to obtain explainability. Then, it comes to the following key question: why the deployed MHMS predicts what they predict. In this article, we shed some light on this meaningful research direction: explainable MHMSs (EMHMS). In EMHMS, the machine doctor could act like a real doctor who can not only make a diagnosis but also describe the patient's symptoms. First, we propose a specific convolutional neural network (CNN) structure, named DecouplEd Feature-Temporal CNN (DEFT-CNN), to balance precision-explainability tradeoff. Specifically, feature information and temporal information have been encoded in different stages of our model. The spatial attention module is added to boost the performance of the model. Then, to explain the decision of the model, we adopt gradient-based methods to generate features and temporal saliency maps highlighting which kinds of features and time steps are keys for the model's predictions. Finally, we conduct the experimental studies on two real datasets to verify the effectiveness of our proposed framework.

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