An intelligent maintenance policy for a latent degradation system

Abstract

This paper looks at the challenge of making maintenance decisions for deteriorating systems when the degradation process leading to failure cannot be directly observed or measured. In this scenario, the system’s health is monitored by observing the progression of a degradation-related marker index, which can be obtained through inspections. To model this configuration, a bivariate gamma process is employed. One component represents the marker process, while the other represents the degradation process, which dictates the time of failure. Two condition-based maintenance (CBM) policies are proposed and analyzed. The first policy is based on a conventional decision structure, utilizing a fixed preventive threshold directly applied to the measured process. The second policy relies on monitoring data related to the marker process to estimate the level of latent degradation at inspections. We demonstrate that the second policy is equivalent to a policy employing an adaptive preventive threshold that sequentially evolves. We provide insights into some key properties associated with this approach. The expected cost rate is calculated and employed for policy optimization. Additionally, a numerical study is presented that showcases the practical implementation of the method and highlights the effectiveness of the second approach, even when the correlation between degradation and the marker process is low.