An asset residual life prediction model based
on expert judgments

Wang, W; Zhang, W

Abstract

An appropriate and accurate residual life prediction for an asset is essential for cost effective and timely maintenance planning and scheduling. The paper reports the use of expert judgments as the additional information to predict a regularly monitored asset’s residual life. The expert judgment is made on the basis of measured condition monitoring parameters, and is treated as a random variable, which may be described by a probability distribution due to the uncertainty involved. Since most expert judgments are in the form of a set of integer numbers, we can either directly use a discrete distribution or use a continuous distribution after some transformation. A key concept used in this paper is condition residual life where the residual life at the point of checking is conditional on, among others, the past expert judgments made on the same assetto date. Stochastic filtering theory is used to predict the residual life given available expert judgments. Artificial, simulated and real data are used for validating and testing the model developed.

Citation

on expert judgments. European Journal of Operational Research, 188, 496-505. https://doi.org/10.1016/j.ejor.2007.03.044

• Journal Article Type: Article
• Publication Date: Jan 1, 2008
• Deposit Date: Nov 24, 2009
• Journal: European Journal of Operational Research
• Print ISSN: 0377-2217
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 188
• Pages: 496-505
• DOI: https://doi.org/10.1016/j.ejor.2007.03.044
• Keywords: Expert judgment; Condition monitoring; Condition based maintenance; Conditional residual life
• Publisher URL: http://dx.doi.org/10.1016/j.ejor.2007.03.044
• Additional Information: Funders : Engineering and Physical Sciences Research Council (EPSRC)