Mechanism-Informed Neural Network: An Interpretable Method for Gearbox Impulsive Fault Feature Extraction

Abstract

Due to high-transmission efficiency, gearboxes have become indispensable components of industrial mechanical equipment. It is paramount for gearbox fault diagnosis to extract discriminant features under strong interferences of industrial scene. However, the extraction performance of current methods is not satisfactory in interpretability and robustness. In this article, an interpretable approach named mechanism-informed neural network (MINN) is proposed for robust impulsive fault feature (IFF) extraction. First, standard auto-encoder is modified based on sparse representation to construct an unsupervised MINN. Second, a mechanism-informed dictionary is designed and embedded into MINN, which brings physical interpretability for the IFF extraction. Third, a two-stage IFF extraction framework is formulated, in which the network parameters are adaptively updated with the proposed joint optimization algorithm to achieve robust IFF extraction. Finally, comparative studies in simulation and experiment are conducted. The results demonstrate that MINN performs better in IFF extraction under strong harmonic interferences. Moreover, the extracted IFF of MINN has been analyzed and interpreted from the view of impulsive fault mechanism, which enhances the reliability.