IBL-AE: An interpretable base learning autoencoder for intelligent fault diagnosis of rotating machinery

Abstract

Deep learning has demonstrated powerful capabilities in fault diagnosis, yet the opaque nature of its internal decision-making severely limits its application in critical engineering scenarios. To address this issue, we propose IBL-AE (Interpretable Basis Learning Autoencoder), a novel deep learning architecture that integrates non-negative basis learning into an autoencoder framework for explainable fault classification. IBL-AE incorporates a non-negative decomposition module inspired by non-negative matrix factorization (NMF) within the latent space, enabling the learned features to be explicitly associated with physically interpretable basis components. Unlike post-hoc interpretability techniques, IBL-AE achieves inherent interpretability by design, as both the network weights and outputs can be visualized and directly linked to key frequency bands indicative of specific fault types. A classification module further utilizes the learned coefficients to make decisions in a human-understandable manner. Extensive experiments on three rotating machinery datasets demonstrate that IBL-AE not only achieves diagnostic accuracy, but also offers interpretable and physically meaningful insights into model behavior, paving the way for more trustworthy and practical deployment in industrial fault diagnosis.