Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery

Abstract

This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for rotating machinery, addressing the challenge of the “black box” nature of machine learning techniques that hampers reliability in automated diagnostic processes. It underscores the growing importance of interpretability in intelligent fault diagnosis (IFD), marking a shift from traditional signal processing methods to machine learning-based approaches that necessitate transparency for trustworthiness. Our review systematically collates and examines the spectrum of interpretability in IFD, distinguishing between post-hoc and ante-hoc strategies. We detail mainstream post-hoc methods, their applications, and critique their limitations, particularly the absence of physical significance. The survey then explores ante-hoc methods that incorporate physical knowledge upfront, enhancing interpretability. By categorizing and evaluating three distinct knowledge embedding approaches, we shed light on their unique applications. Conclusively, we highlight emerging research directions and challenges in the field, aiming to equip readers with a nuanced understanding of current methodologies and inspire future studies in making IFD more reliable and interpretable.