Development of an Explainable Fault Diagnosis Framework Based on Sensor Data Imagification: A Case Study of the Robotic Spot-Welding Process

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In recent years, various advanced fault diagnostic models applying deep learning techniques have been proposed, but the confidence in model prediction in the industrial field is still low. Therefore, a method is required to establish a reliable fault diagnostic model that can provide an understandable rationale for the prediction result. This article develops an explainable fault diagnosis framework that infers the causal relationship of failure by combining domain knowledge. A novel data imagification methodology that generates fuzzy-based energy pattern image (FEPI) data using sensor signal is applied to the framework, and the physical interpretability of the FEPI data plays a key role in inferring the causality of the fault. Furthermore, a case study of the robotic spot-welding process is conducted to validate the proposed framework. Convolutional neural network (CNN)-based fault diagnostic model is trained by the FEPI data, and the result of gradient-weighted class activation mapping that traces the critical region for fault classification is interpreted by the domain knowledge to infer the failure causes. Finally, the accuracy of fault diagnosis and the performance of causal inference for the explainable fault diagnosis framework are verified together.