A practical method for failure analysis using incomplete warranty data

K. Mohan, B. Cline, J. Akers
{"title":"A practical method for failure analysis using incomplete warranty data","authors":"K. Mohan, B. Cline, J. Akers","doi":"10.1109/RAMS.2008.4925794","DOIUrl":null,"url":null,"abstract":"The use of warranty claims data to determine the failure characteristics of a product is well documented. Typically, the failure distribution and its parameters are determined using product manufacturing data for each month of production and the corresponding monthly failure counts derived from the warranty claims. If the data is collected systematically, the product ages at the times of failure can be derived. Classical methods are then used to determine the failure time distribution and parameters. However, our experience shows that, in many cases, it may not be possible to know the failure ages of components. The information available each month might be limited to the volume of shipments and total claims or product returns. In such cases, the data hides the component age at the time of failure. In this paper, we show that when the failure history information is incomplete, the failure distribution of the product can be determined using Bayesian analysis techniques applicable for handling incomplete data. We apply the popular Expectation-Maximization (EM) algorithm to find the Maximum Likelihood Estimates (MLE) of the failure distribution parameters using incomplete data. The effectiveness of the EM algorithm is compared using several sets of incomplete warranty data generated using simulation. We observed that the EM algorithm is powerful in capturing the hidden failure patterns from the incomplete warranty data.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Annual Reliability and Maintainability Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMS.2008.4925794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12

Abstract

The use of warranty claims data to determine the failure characteristics of a product is well documented. Typically, the failure distribution and its parameters are determined using product manufacturing data for each month of production and the corresponding monthly failure counts derived from the warranty claims. If the data is collected systematically, the product ages at the times of failure can be derived. Classical methods are then used to determine the failure time distribution and parameters. However, our experience shows that, in many cases, it may not be possible to know the failure ages of components. The information available each month might be limited to the volume of shipments and total claims or product returns. In such cases, the data hides the component age at the time of failure. In this paper, we show that when the failure history information is incomplete, the failure distribution of the product can be determined using Bayesian analysis techniques applicable for handling incomplete data. We apply the popular Expectation-Maximization (EM) algorithm to find the Maximum Likelihood Estimates (MLE) of the failure distribution parameters using incomplete data. The effectiveness of the EM algorithm is compared using several sets of incomplete warranty data generated using simulation. We observed that the EM algorithm is powerful in capturing the hidden failure patterns from the incomplete warranty data.
使用不完整保修数据进行故障分析的实用方法
使用保修期索赔数据来确定产品的故障特征是有据可查的。通常,故障分布及其参数是使用每个月生产的产品制造数据和从保修索赔中得出的相应的每月故障计数来确定的。如果系统地收集数据,则可以推导出产品在故障时的年龄。然后采用经典方法确定失效时间分布和参数。然而,我们的经验表明,在许多情况下,可能不可能知道组件的失效年龄。每个月可用的信息可能仅限于出货量和总索赔或产品退货。在这种情况下,数据隐藏了故障时组件的年龄。在本文中,我们证明了当失效历史信息不完整时,可以使用适用于处理不完整数据的贝叶斯分析技术确定产品的失效分布。我们应用流行的期望最大化(EM)算法在不完全数据下找到故障分布参数的最大似然估计(MLE)。利用仿真生成的几组不完整保修数据,比较了EM算法的有效性。我们观察到,EM算法在从不完整的保修数据中捕获隐藏的故障模式方面功能强大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信