Interpretable sparse learned weights and their entropy based quantification for online machine health monitoring

Abstract

Incipient fault detection and diagnosis provide a firm grounding for machine health monitoring. Nevertheless, fault signatures at the time of an incipient fault are extremely weak and easily submerged by interference. Currently, signal processing based approaches and machine learning based approaches have been studied for incipient fault detection and diagnosis. However, the former requires relevant technicians with expert knowledge, while the latter needs a substantial volume of labeled samples without model interpretability. In this study, a physics-informed learning framework that integrates weight-based sparse degradation modeling with entropy based indicators is proposed to realize online incipient fault detection and diagnosis. Firstly, based on available normal baseline data and real-time data, a weight-based sparse degradation model is proposed to continually update physics-informed model weights so that weak fault characteristics indicated by learned weights can be considerably enhanced. Meanwhile, this study introduces a family of entropy based indicators for machine health monitoring and their performances are thoroughly investigated based on simulation and experimental studies, which aims to quantify amplified fault characteristics revealed by the continuously updated model weights for online incipient fault detection. Two case studies show that the proposed methodology has better detection ability and sensitivity than classical health indicators for incipient bearing faults. Since the proposed methodology does not demand fault data for model establishment, it is closer to real engineering applications and has more engineering meanings. Moreover, physics-informed model weights can automatically capture informative frequencies for immediate diagnosis and further analysis.