Quantifying the Impact of Prognostic Distance on Average Cost per Cycle

Abstract

Prognostics and health management (PHM) is transforming reliability engineering with methods to enhance safety by accurately estimating end of useful life, thereby recommending maintenance of critical components and systems to manage cost. Previous studies emphasized degradation modeling and algorithms to improve state of health predictions. However, most of these techniques focused on improving the accuracy of predictions within a single maintenance interval, while fewer studies considered the effectiveness of alternative degradation models over multiple successive maintenance intervals. This paper develops a measure based on concepts from maintenance theory to provide a framework to objectively compare the effectiveness of existing and future battery degradation models over multiple maintenance intervals. The approach quantifies the impact of prognostic distance on average cost per cycle during the lifetime of a system. The approach is applied to state of health prediction for lithium-ion batteries, which are widely used in various mission-critical systems. The results indicate that the approach can be used to select a prognostic distance that minimizes average cost. The approach can thus evaluate models to select a suitable prognostic distance.