On the Prediction of Future Failures for a Repairable Equipment Subject to Overhauls

Pulcini, Gianpaolo

doi:10.1081/sta-100002145

Cited by 9 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several authors have extensively researched the use of the power law process for evaluating hardware reliability growth and identifying software failures. For instance, Pulcini (2001) delved into Bayesian prediction methods for anticipating future failure times and the number of failures in a specific time interval for a repairable system that undergoes minimal repairs and periodic overhauls. Sen (2002) explored the Bayesian prediction of the Weibull intensity, while Pfefferman and Cernuschi-Frias (2002) introduced a nonparametric prediction approach.…”

Section: Power Law Process (Plp)mentioning

confidence: 99%

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Alsultan,

Sulaiman

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

Non-homogeneous Poisson process with power law intensity function has often been used as a model for describing the failure pattern of repairable systems. Maximum likelihood and Bayesian estimation are used to estimate model parameters. Simulation and realistic application are used and represented by shutting down the gas power plant in Mosul. Stops in hours are designed with the power law random process model in order to obtain a model that represents the average stop time of the units throughout the study period in the best way. The results of the application on the data of the three concerned stations show that the Bayes estimate is better than the maximum likelihood estimate. This proves that the Bayes methods are very accurate and effective in estimating the rate of occurrence parameters.

show abstract

Section: Power Law Process (Plp)mentioning

confidence: 99%

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Alsultan,

Sulaiman

2024

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…the equipment is improved to a seemingly new condition "better than old" by maintenance action and for the worse repair case i.e. the equipment is brought to a condition "worse than old" than it was in just before the maintenance Reliability modeling and prediction action (Lindqvist, 2006;Muralidharan, 2008;Guo et al, 2000;Pham, 2003;Pulcini, 2001;Shin et al, 1996). Although, it is highly implausible that, any maintenance/repair action brings equipments to "same as new" condition, the renewal process (RP) has been used for perfect repair case (Ascher and Feingold, 1984;Thompson, 1988).…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling and prediction using classical and Bayesian approach

Ghosh

Majumdar

2011

International Journal of Quality &Amp; Reliability Management

View full text Add to dashboard Cite

Purpose -The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter-failure times. Design/methodology/approach -The failure patterns of five different machine systems were modeled with NHPP-log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non-informative independent priors to derive the posterior densities of the model parameters of NHPP-LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals. Findings -For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that "ROCOF (rate of occurrence of failures) would exceed its upper threshold limit" in future time frames were uniformly higher for these machine systems than those obtained with the classical approach. Practical implications -This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter-failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy. Originality/value -With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non-informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter-failure times and estimating their reliability in future time frames.

show abstract

Mechanical Reliability and Maintenance Models

Pulcini¹

Handbook of Reliability Engineering

View full text Add to dashboard Cite

On the Prediction of Future Failures for a Repairable Equipment Subject to Overhauls

Cited by 9 publications

References 9 publications

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Bayesian Estimation of Power Law Function in Non-homogeneous Poisson Process Applied in Mosul Gas Power Plant – Iraq

Reliability modeling and prediction using classical and Bayesian approach

Mechanical Reliability and Maintenance Models

Contact Info

Product

Resources

About