TECHNICAL PAPERS
Feb 1, 2008

Probabilistic Failure Prediction for Deteriorating Pipelines: Nonparametric Approach

Publication: Journal of Performance of Constructed Facilities
Volume 22, Issue 1

Abstract

Component failures in water distribution systems are usually predicted by parametric models where the model parameters are determined by projecting the past failure rates of the component to the future. This paper shows that in such techniques, failures are implicitly assumed to be stationary random processes. However, due to the nonstationary nature of some influencing factors, this assumption may lead to inaccurate predictions. A new nonparametric technique is developed for failure prediction of classes of pipes considering this nonstationary process. The presented technique uses limited data that are typical to the databases of water distribution systems. In this method, maximum likelihood estimates of the probability of future failures are calculated and used, both to predict the number of failures occurring within a specified period of time in future, and to provide some lower and upper bounds (confidence intervals) for the estimations. This technique is applied to predict the failures of water pipes in western suburbs of Melbourne. Results of the predictions are compared with the empirical results from a failure record. Deviation of these predictions from empirical measures in terms of both rejection rates and mean-square errors of predictions are acceptable.

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Acknowledgments

This research was supported by City West Water Company Pty Ltd., Victoria, Australia.

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Published In

Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 22Issue 1February 2008
Pages: 45 - 53

History

Received: Sep 12, 2006
Accepted: Jun 19, 2007
Published online: Feb 1, 2008
Published in print: Feb 2008

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Authors

Affiliations

Azam Dehghan [email protected]
Ph.D. Student, Faculty of Engineering and Industrial Sciences, Swinburne Univ. of Technology, P.O. Box 218, Hawthorn, Victoria 3122, Australia. E-mail: [email protected]
Kerry J. McManus [email protected]
Associate Professor, Faculty of Engineering and Industrial Sciences, Swinburne Univ. of Technology, P.O. Box 218, Hawthorn, Victoria 3122, Australia. E-mail: [email protected]
Emad F. Gad [email protected]
Associate Professor, Faculty of Engineering and Industrial Sciences, Swinburne Univ. of Technology, P.O. Box 218, Hawthorn, Victoria 3122, Australia. E-mail: [email protected]

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