Modeling Failure Modes for Residual Life Prediction Using Stochastic Filtering Theory

This paper reports on a theoretical Bayesian modeling development for residual life prediction in the context of condition-based maintenance. At each monitoring point during a components lifetime, the stochastic filter is used to establish a posterior conditional probability density function (PDF) for the residual life. The PDF can then be used in the evaluation of maintenance and replacement decisions. The research documented in this paper extends the modeling principles in accordance with a practical consideration recognized in a number of previous case applications. Many monitoring scenarios provide evidence that the operational components involved may potentially be subject to a number of individual distinct failure modes, rather than a single dominant failure mode as modeled previously. The modeling procedure proposed to handle this scenario is based on the assumption that an individual monitored component will fail according to one of a number of predefined failure modes. Individ...

• Type: Journal
• Year: 2010
• Where: TR
• Authors: Matthew J. Carr, Wenbin Wang
• Keywords: Failure Mode, Hardware, Residual Life, Residual Life Prediction, TR 2010