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International Journal of Quality, Statistics, and Reliability
Volume 2011 (2011), Article ID 681210, 8 pages
http://dx.doi.org/10.1155/2011/681210
Research Article

Bayesian Prediction of the Overhaul Effect on a Repairable System with Bounded Failure Intensity

Department of Operational Research, University of Delhi, Delhi 7, India

Received 13 December 2010; Revised 20 June 2011; Accepted 21 June 2011

Academic Editor: Ratna Babu Chinnam

Copyright © 2011 Preeti Wanti Srivastava and Nidhi Jain. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper deals with the Bayes prediction of the future failures of a deteriorating repairable mechanical system subject to minimal repairs and periodic overhauls. To model the effect of overhauls on the reliability of the system a proportional age reduction model is assumed and the 2-parameter Engelhardt-Bain process (2-EBP) is used to model the failure process between two successive overhauls. 2-EBP has an advantage over Power Law Process (PLP) models. It is found that the failure intensity of deteriorating repairable systems attains a finite bound when repeated minimal repair actions are combined with some overhauls. If such a data is analyzed through models with unbounded increasing failure intensity, such as the PLP, then pessimistic estimates of the system reliability will arise and incorrect preventive maintenance policy may be defined. On the basis of the observed data and of a number of suitable prior densities reflecting varied degrees of belief on the failure/repair process and effectiveness of overhauls, the prediction of the future failure times and the number of failures in a future time interval is found. Finally, a numerical application is used to illustrate the advantages from overhauls and sensitivity analysis of the improvement parameter carried out.