Threshold-Varying Assessment for Prognostics and Health Management

Abstract

Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.