Abstract
Remaining useful life (RUL) estimation by monitoring in-situ health of components and systems permits decision-makings in the condition based maintenance policy relying on actual operational states. In this paper, we consider systems subject to competing failures, and determine the remaining useful life distribution at the end of monitoring time. Soft failures consider both the degradation and damage from a shock process to the system, whereas, the hard failures are based on shock process. Both failures are connected by the ratio constant between damages and loads. The particle filter is applied for the statement estimation and online prediction of remaining useful life based on degradation and shock failure risks in the framework of prognostics and health management (PHM). A Micro-Electro-Mechanical System example demonstrates numerical analysis.
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