A health indicator enabling both first predicting time detection and remaining useful life prediction: Application to rotating machinery

Abstract

Remaining Useful Life (RUL) prediction is vital for system functionality. Non-end-to-end approaches is an important type of RUL prediction approaches for their important application in industrial scenarios. In non-end-to-end approaches, Health Indicator (HI) construction is a critical aspect. However, existing HI construction approaches ignore First Predicting Time (FPT) detection, leading to increased domain knowledge demand and system health comprehension difficulty. To address this issue, this paper proposes a multi-objective-optimization-based HI construction approach enabling both FPT detection and RUL prediction. A novel metric called the monotonicity strength index is proposed to address the limitation of the conventional monotonicity. The constructed HI possesses the ability to indicate FPT by taking the detectability metric as an optimization objective. The optimization problem is solved by the combination of the multi-objective ant lion optimizer and the entropy weight method. The superiority of this HI is demonstrated through experiments on the widely used IMS bearing dataset and a gearbox dataset.