Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging Markovian deterioration

An often seen practice of preventive maintenance (PM) is to construct a machine's reliability model based on its historical failure records. The reliability model is then used to determine the PM schedule by minimizing the machine's long-run operation cost or average machine downtime. Machines in many hi-tech manufacturing sectors are using sophisticated sensor technologies to provide sufficient immediate online data for real-time observation of equipment condition. Not only is the historical data but also the real time condition now available for scheduling a more effective PM policy. This research is to determine an effective PM policy based on real-time observations of equipment condition. We first use the multivariate process capability index to integrate the equipment's multiple parameters into an overall equipment health index. This health index serves as the basis for real-time health prognosis under an aging Markovian deterioration model. A dynamic PM schedule is then determined based on the health prognosis.