A Failure Time Prediction Method for Condition-Based Maintenance

ABSTRACT

Due to uncertainties in material properties and use conditions, reliability predictions are often subject to considerable error. Such inaccurate predictions lead to maintenance decisions that are expensive or not able to prevent failures. This article proposes a Bayesian approach for failure time prediction of degrading components from condition data that accounts for uncertainties and incorporates prior information on degradation behavior via prior distributions. The approach is based on posterior sampling to handle more general statistical models. Simulation examples are presented to show that by incorporating condition monitoring data or including prior knowledge the effectiveness of maintenance decisions can be significantly improved. Application of the approach in a real setting is illustrated using data from the literature.

KEYWORDS:

• Bayesian analysis
• condition-based maintenance
• degradation modeling
• reliability

ACKNOWLEDGMENT

The author acknowledges the comments and suggestions from the Associate Editor and two anonymous referees whose input has improved the quality of the article significantly.

Notes

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/lqen.