Show simple item record

dc.contributor.authorAttanayake, Dona Nayomi Sandarekha
dc.date.accessioned2020-08-04T02:15:42Z
dc.date.available2020-08-04T02:15:42Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11343/241687
dc.description© 2019 Dona Nayomi Sandarekha Attanayake
dc.description.abstractOperation and maintenance of a fleet always require a high level of readiness, reduced cost, and improved safety. In order to achieve these goals, it is essential to develop and determine an appropriate maintenance programme for the components in use. A failure analysis involving failure model selection, robust parameter estimation, probabilistic decision making, and assessing the cost-effectiveness of the decisions are the key to the selection of a proper maintenance programme. Two significant challenges faced in failure analysis studies are, minimizing the uncertainty associated with model selection and making strategic decisions based on few observed failures. In this thesis, we try to resolve some of these problems and evaluate the cost-effectiveness of the selections. We focus on choosing the best model from a model space and robust estimation of quantiles leading to the selection of optimal repair and replacement time of units. We first explore the repair and replacement cost of a unit in a system. We design a simulation study to assess the performance of the parameter estimation methods, maximum likelihood estimation (MLE), and median rank regression method (MRR) in estimating quantiles of the Weibull distribution. Then, we compare the models; Weibull, gamma, log-normal, log-logistic, and inverse-Gaussian in failure analysis. With an example, we show that the Weibull and the gamma distributions provide competing fits to the failure data. Next, we demonstrate the use of Bayesian model averaging in accounting for that model uncertainty. We derive an average model for the failure observations with respective posterior model probabilities. Then, we illustrate the cost-effectiveness of the selected model by comparing the distribution of the total replacement and repair cost. In the second part of the thesis, we discuss the prior information. Initially, we assume, the parameters of the Weibull distribution are dependent by a function of the form rho = sigma/mu and re-parameterize the Weibull distribution. Then we propose a new Jeffreys’ prior for the parameters mu and rho. Finally, we designed a simulation study to assess the performance of the new Jeffreys’ prior compared to the MLE.
dc.rightsTerms and Conditions: Copyright in works deposited in Minerva Access is retained by the copyright owner. The work may not be altered without permission from the copyright owner. Readers may only download, print and save electronic copies of whole works for their own personal non-commercial use. Any use that exceeds these limits requires permission from the copyright owner. Attribution is essential when quoting or paraphrasing from these works.
dc.subjectMaximum likelihood estimation
dc.subjectBayesian Analysis
dc.subjectBayesian Model Averaging
dc.subjectMedian Rank Regression
dc.subjectFailure Analysis
dc.subjectWeibull distribution
dc.subjectLog-location scale distributions
dc.subjectJeffreys' Prior
dc.subjectCost Analysis
dc.subjectRobustness
dc.subjectParameter estimation
dc.subjectProbabilistic decision making
dc.subjectCensored data
dc.titleRisk Analysis and Probabilistic Decision Making for Censored Failure Data
dc.typePhD thesis
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.affiliation.facultyScience
melbourne.thesis.supervisornameAndrew Robinson
melbourne.contributor.authorAttanayake, Dona Nayomi Sandarekha
melbourne.tes.fieldofresearch1010401 Applied Statistics
melbourne.tes.fieldofresearch2091008 Manufacturing Safety and Quality
melbourne.tes.confirmedtrue
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record