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Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model

The degradation models are often applied on the degradation data for studying time-to-failure distribution. In this study, the Bayesian approach is applied on the power degradation model for estimating the parameters of the time-to-failure distribution and its percentiles. Two different distribution...

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Main Authors: Laila Naji Ba Dakhn,, Mohd Aftar Abu Bakar,, Razik Ridzuan Mohd Tajuddin,, Kamarulzaman Ibrahim,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:http://journalarticle.ukm.my/24506/1/SS%2024.pdf
http://journalarticle.ukm.my/24506/
https://www.ukm.my/jsm/english_journals/vol53num9_2024/contentsVol53num9_2024.html
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spelling my-ukm.journal.245062024-11-12T07:28:24Z http://journalarticle.ukm.my/24506/ Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model Laila Naji Ba Dakhn, Mohd Aftar Abu Bakar, Razik Ridzuan Mohd Tajuddin, Kamarulzaman Ibrahim, The degradation models are often applied on the degradation data for studying time-to-failure distribution. In this study, the Bayesian approach is applied on the power degradation model for estimating the parameters of the time-to-failure distribution and its percentiles. Two different distributions are assumed for the degradation parameter of the model. The degradation parameter is firstly assumed to follow the skew-normal distribution with three jointly independently distributed parameters such that the gamma prior is assumed for the shape parameter, while the scale and the location parameters are assumed uniform. The second distribution assumed for the degradation parameter is the log-logistic distribution with two jointly independent random parameters where the shape parameter is assumed gamma, while the scale parameter is assumed uniform. Based on the Gibbs sampling method carried out under the JAGS platform, the models considered are applied on the simulated data and the NASA turbofan Jet engine dataset and the results found are compared. In modeling the time-to-failure distribution, it is shown that based on the simulated data and real data, the Bayesian approach for the power degradation model with the skew-normal degradation parameter outperformed the Bayesian approach for the power degradation model with the log-logistic degradation parameter. Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24506/1/SS%2024.pdf Laila Naji Ba Dakhn, and Mohd Aftar Abu Bakar, and Razik Ridzuan Mohd Tajuddin, and Kamarulzaman Ibrahim, (2024) Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model. Sains Malaysiana, 53 (9). pp. 3215-3227. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol53num9_2024/contentsVol53num9_2024.html
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description The degradation models are often applied on the degradation data for studying time-to-failure distribution. In this study, the Bayesian approach is applied on the power degradation model for estimating the parameters of the time-to-failure distribution and its percentiles. Two different distributions are assumed for the degradation parameter of the model. The degradation parameter is firstly assumed to follow the skew-normal distribution with three jointly independently distributed parameters such that the gamma prior is assumed for the shape parameter, while the scale and the location parameters are assumed uniform. The second distribution assumed for the degradation parameter is the log-logistic distribution with two jointly independent random parameters where the shape parameter is assumed gamma, while the scale parameter is assumed uniform. Based on the Gibbs sampling method carried out under the JAGS platform, the models considered are applied on the simulated data and the NASA turbofan Jet engine dataset and the results found are compared. In modeling the time-to-failure distribution, it is shown that based on the simulated data and real data, the Bayesian approach for the power degradation model with the skew-normal degradation parameter outperformed the Bayesian approach for the power degradation model with the log-logistic degradation parameter.
format Article
author Laila Naji Ba Dakhn,
Mohd Aftar Abu Bakar,
Razik Ridzuan Mohd Tajuddin,
Kamarulzaman Ibrahim,
spellingShingle Laila Naji Ba Dakhn,
Mohd Aftar Abu Bakar,
Razik Ridzuan Mohd Tajuddin,
Kamarulzaman Ibrahim,
Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
author_facet Laila Naji Ba Dakhn,
Mohd Aftar Abu Bakar,
Razik Ridzuan Mohd Tajuddin,
Kamarulzaman Ibrahim,
author_sort Laila Naji Ba Dakhn,
title Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
title_short Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
title_full Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
title_fullStr Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
title_full_unstemmed Bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
title_sort bayesian approach for estimating the parameters of the time-to-failure distribution and its percentiles under power degradation model
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2024
url http://journalarticle.ukm.my/24506/1/SS%2024.pdf
http://journalarticle.ukm.my/24506/
https://www.ukm.my/jsm/english_journals/vol53num9_2024/contentsVol53num9_2024.html
_version_ 1816131608719130624
score 13.244413

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