威布尔林德利分布

D. A. Magfira, D. Lestari, S. Nurrohmah
{"title":"威布尔林德利分布","authors":"D. A. Magfira, D. Lestari, S. Nurrohmah","doi":"10.1063/5.0059262","DOIUrl":null,"url":null,"abstract":"In reliability systems, there are known two types of systems namely series systems and parallel systems. In the series system, failure will occur if any of the possible events happen. Applications of the series system analysis also varies from inspecting the durability of manufactured products to examining diseases in human. Therefore, several distributions have been introduced to model failure data in series system. However, these distributions cannot model data with bathtub shaped hazard function even though it is the one mostly found in real life situation. As a result, distribution which can model lifetime data in series system with bathtub-shaped hazard function has to be developed. In real life application, there is condition where failure could occur caused by several independent events and has a bathtub shaped hazard function, for example engineering cases and competing risk. Weibull Lindley distribution, which was introduced by Asgharzadeh et al. (2018), is developed to solve the problem. As Weibull Lindley distribution describes lifetime data of an object that can experience failure caused by 2 possible events. It can model data with increasing, decreasing and bathtub shaped hazard function. Asgharzadeh et al. (2018) only show the modeling of Weibull Lindley distribution in medical field which is competing risk data. This paper discusses the process of forming the Weibull Lindley distribution, its properties and parameter estimation using the maximum likelihood method. In addition, the application of Weibull Lindley distribution in engineering field which is the lifetime data of machine consists of two independent components paired in series also be discussed.","PeriodicalId":20561,"journal":{"name":"PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020)","volume":"33 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Weibull Lindley distribution\",\"authors\":\"D. A. Magfira, D. Lestari, S. Nurrohmah\",\"doi\":\"10.1063/5.0059262\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In reliability systems, there are known two types of systems namely series systems and parallel systems. In the series system, failure will occur if any of the possible events happen. Applications of the series system analysis also varies from inspecting the durability of manufactured products to examining diseases in human. Therefore, several distributions have been introduced to model failure data in series system. However, these distributions cannot model data with bathtub shaped hazard function even though it is the one mostly found in real life situation. As a result, distribution which can model lifetime data in series system with bathtub-shaped hazard function has to be developed. In real life application, there is condition where failure could occur caused by several independent events and has a bathtub shaped hazard function, for example engineering cases and competing risk. Weibull Lindley distribution, which was introduced by Asgharzadeh et al. (2018), is developed to solve the problem. As Weibull Lindley distribution describes lifetime data of an object that can experience failure caused by 2 possible events. It can model data with increasing, decreasing and bathtub shaped hazard function. Asgharzadeh et al. (2018) only show the modeling of Weibull Lindley distribution in medical field which is competing risk data. This paper discusses the process of forming the Weibull Lindley distribution, its properties and parameter estimation using the maximum likelihood method. In addition, the application of Weibull Lindley distribution in engineering field which is the lifetime data of machine consists of two independent components paired in series also be discussed.\",\"PeriodicalId\":20561,\"journal\":{\"name\":\"PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020)\",\"volume\":\"33 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0059262\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/5.0059262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

在可靠性系统中,有两种已知的系统,即串联系统和并联系统。在串联系统中,任何可能发生的事件都将导致故障。该系列系统分析的应用也从检验制造产品的耐久性到检验人类疾病。因此,在串联系统中引入了几种分布来模拟故障数据。然而,这些分布不能用浴缸形的危险函数来模拟数据,尽管浴缸形的危险函数是现实生活中最常见的。因此,必须建立一种具有浴缸形危险函数的串联系统寿命数据的分布模型。在实际应用中,存在由几个独立事件引起的故障情况,并且具有浴缸形的危险函数,例如工程案例和竞争风险。为了解决这个问题,Asgharzadeh等人(2018)提出了Weibull Lindley分布。由于Weibull Lindley分布描述了一个对象的生命周期数据,该对象可能经历由两种可能事件引起的故障。它可以用增加、减少和浴盆形的危险函数对数据进行建模。Asgharzadeh et al.(2018)只展示了Weibull Lindley分布在医疗领域的建模,这是竞争风险数据。本文讨论了Weibull - Lindley分布的形成过程、分布的性质以及用极大似然方法进行参数估计。此外,还讨论了威布尔林德利分布在工程领域的应用,即机器的寿命数据由两个独立的组件串联而成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Weibull Lindley distribution
In reliability systems, there are known two types of systems namely series systems and parallel systems. In the series system, failure will occur if any of the possible events happen. Applications of the series system analysis also varies from inspecting the durability of manufactured products to examining diseases in human. Therefore, several distributions have been introduced to model failure data in series system. However, these distributions cannot model data with bathtub shaped hazard function even though it is the one mostly found in real life situation. As a result, distribution which can model lifetime data in series system with bathtub-shaped hazard function has to be developed. In real life application, there is condition where failure could occur caused by several independent events and has a bathtub shaped hazard function, for example engineering cases and competing risk. Weibull Lindley distribution, which was introduced by Asgharzadeh et al. (2018), is developed to solve the problem. As Weibull Lindley distribution describes lifetime data of an object that can experience failure caused by 2 possible events. It can model data with increasing, decreasing and bathtub shaped hazard function. Asgharzadeh et al. (2018) only show the modeling of Weibull Lindley distribution in medical field which is competing risk data. This paper discusses the process of forming the Weibull Lindley distribution, its properties and parameter estimation using the maximum likelihood method. In addition, the application of Weibull Lindley distribution in engineering field which is the lifetime data of machine consists of two independent components paired in series also be discussed.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Singular Integral Operators, Quantitative Flatness, and Boundary Problems Mirzakhani’s Curve Counting and Geodesic Currents The conception of national nuclear emergency preparedness system from the state defense perspective Computer security regulation for nuclear installation: A suggestion Optimal reinsurance combination of quota-share and stop-loss reinsurance based on conditional-tail-expectation (CTE) optimization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1