Predictive maintenance management using sensor-based degradation models

Abstract

This paper presents a sensory-updated degradation-based predictive maintenance (SUDM) policy. The proposed maintenance policy utilizes contemporary degradation models that combine component-specific real-time degradation signals, acquired during operation, with degradation and reliability characteristics of the component’s population to predict and update the residual life distribution of the component. By capturing the latest degradation state of the components being monitored, the updating process provides more accurate estimates of the remaining life. With the aid of a stopping rule, maintenance routines are scheduled based on the most recently updated residual life distributions. The performance of the proposed maintenance policy is evaluated using a simulation model of a simple manufacturing cell. Frequency of unexpected failures and overall maintenance costs are evaluated and compared with two other benchmark maintenance policies, a reliability-based and a conventional degradation-based maintenance policy (without any sensor-based updating).