Lifelong Monitoring of Bearing-Rotor Systems Over Whole Life Cycle: An Emerging Paradigm

Abstract

Lifelong learning (LL) has proven successful in computer vision and natural language processing, but its applications in condition monitoring are still largely understudied. To bridge this gap, this article innovatively proposed a LL-based condition monitoring framework for rotating machinery over the whole life cycle. First, the development of LL is reviewed and summarized. Inspired by the core idea of knowledge maintenance and transfer in LL, the innovation of this article is to integrate data-driven modeling with LL to extract process knowledge-driven features for autonomous condition monitoring. This opens up an emerging lifelong monitoring paradigm for mechanical systems. Based on the extracted features learned from the data-driven model, a threshold construction method and an online monitoring strategy are integrated into the monitoring framework. Finally, the utility of the proposed framework is demonstrated through the rolling bearing performance degradation and shaft fatigue fracture test cases. Compared to the state-of-the-art monitoring methods, the proposed framework shows significantly improved adaptability and reliability.