Data-Driven Remaining Useful Life Prediction to Plan Operations Shutdown and Maintenance of an Industrial Plant

摘要

Predicting remaining useful life (RUL) of critical machines in heavy maintenance industries, such as oil refineries and upgrading, is crucial to deliver robust and cost-effective plans for resource procurement and maintenance scheduling in a challenging and constrained work environment. Statistical methods have been extensively utilized based on condition monitoring (CM) and sensor data. In this paper, we investigate the use of statistical methods in RUL prediction and conduct the taxonomy of those methods to provide better understanding of their potential application in the context of asset maintenance management for an industrial plant. Then the application of statistical learning methods for data-driven RUL prediction is illustrated with sensor data collected from a running plant. The research deliverable is intended to provide data-driven RUL predictions as part of plant asset management system and shed light on decisions directly pertaining to industrial plant maintenance management.