Robust Interpretable Deep Learning for Intelligent Fault Diagnosis of Induction Motors

Abstract

In modern manufacturing processes, motivations for automatic fault diagnosis (FD) are increasingly growing as a result of the great trends toward achieving zero breakdowns. Induction motors (IMs) represent a critical part in most of the applications. Due to its high potential of automatic feature extraction, the deep learning (DL)-based FD of IM has recently been introduced and has essentially emphasized on the diagnosis using the vibration analysis. However, this approach has not received considerable attention when using the current analysis, although it represents a cost-effective alternative. Moreover, the already implemented DL architectures are still suffering from lack of physical interpretability. In this article, a new DL architecture called deep-SincNet is implemented for a multi-FD task. The proposed end-to-end scheme automatically learns the fault features from the raw motor current and accordingly finalizes the FD process. A high accuracy for several separated and combined faults, a more physical interpretability, a high robustness against noisy environments, and a significant gain in implementation cost prove the competitive performance of the proposed approach.