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An asset residual life prediction model based on expert judgments

Wang, W and Zhang, W 2008, 'An asset residual life prediction model based on expert judgments' , European Journal of Operational Research, 188 , pp. 496-505.

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    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.

    Item Type: Article
    Uncontrolled Keywords: Expert judgment; Condition monitoring; Condition based maintenance; Conditional residual life
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA275 Mathematical Statistics
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Business & Law > Salford Business School > Operations and Global Logistics Management
    Journal or Publication Title: European Journal of Operational Research
    Publisher: Elsevier
    Refereed: Yes
    ISSN: 0377-2217
    Funders: Engineering and Physical Sciences Research Council (EPSRC)
    Depositing User: W Wang
    Date Deposited: 24 Nov 2009 09:40
    Last Modified: 20 Jan 2014 20:53
    URI: http://usir.salford.ac.uk/id/eprint/2541

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