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Title: Estimating a Service-Life Distribution Based on Production Counts and a Failure Database

A manufacturer wanted to compare the service-life distributions of two similar products. These concern product lifetimes after installation (not manufacture). For each product, there were available production counts and an imperfect database providing information on failing units. In the real case, these units were expensive repairable units warrantied against repairs. Failure (of interest here) was relatively rare and driven by a different mode/mechanism than ordinary repair events (not of interest here). Approach: Data models for the service life based on a standard parametric lifetime distribution and a related limited failure population were developed. These models were used to develop expressions for the likelihood of the available data that properly accounts for information missing in the failure database. Results: A Bayesian approach was employed to obtain estimates of model parameters (with associated uncertainty) in order to investigate characteristics of the service-life distribution. Custom software was developed and is included as Supplemental Material to this case study. One part of a responsible approach to the original case was a simulation experiment used to validate the correctness of the software and the behavior of the statistical methodology before using its results in the application, and an example of such an experiment is includedmore » here. Because of confidentiality issues that prevent use of the original data, simulated data with characteristics like the manufacturer’s proprietary data are used to illustrate some aspects of our real analyses. Lastly, we also note that, although this case focuses on rare and complete product failure, the statistical methodology provided is directly applicable to more standard warranty data problems involving typically much larger warranty databases where entries are warranty claims (often for repairs) rather than reports of complete failures.« less
Authors:
 [1] ;  [2] ;  [3]
  1. West Virginia Univ., Morgantown, WV (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Iowa State Univ., Ames, IA (United States)
Publication Date:
Report Number(s):
LA-UR-15-26383
Journal ID: ISSN 0022-4065
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Journal of Quality Technology
Additional Journal Information:
Journal Volume: 49; Journal Issue: 2; Journal ID: ISSN 0022-4065
Publisher:
American Society for Quality (ASQ)
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 99 GENERAL AND MISCELLANEOUS; Case study; Bayesian methods; Parameters; Manufacturing; Simulation experiments; Warranties; Failure
OSTI Identifier:
1392800

Ryan, Kenneth J., Hamada, Michael Scott, and Vardeman, Stephen B.. Estimating a Service-Life Distribution Based on Production Counts and a Failure Database. United States: N. p., Web.
Ryan, Kenneth J., Hamada, Michael Scott, & Vardeman, Stephen B.. Estimating a Service-Life Distribution Based on Production Counts and a Failure Database. United States.
Ryan, Kenneth J., Hamada, Michael Scott, and Vardeman, Stephen B.. 2017. "Estimating a Service-Life Distribution Based on Production Counts and a Failure Database". United States. doi:. https://www.osti.gov/servlets/purl/1392800.
@article{osti_1392800,
title = {Estimating a Service-Life Distribution Based on Production Counts and a Failure Database},
author = {Ryan, Kenneth J. and Hamada, Michael Scott and Vardeman, Stephen B.},
abstractNote = {A manufacturer wanted to compare the service-life distributions of two similar products. These concern product lifetimes after installation (not manufacture). For each product, there were available production counts and an imperfect database providing information on failing units. In the real case, these units were expensive repairable units warrantied against repairs. Failure (of interest here) was relatively rare and driven by a different mode/mechanism than ordinary repair events (not of interest here). Approach: Data models for the service life based on a standard parametric lifetime distribution and a related limited failure population were developed. These models were used to develop expressions for the likelihood of the available data that properly accounts for information missing in the failure database. Results: A Bayesian approach was employed to obtain estimates of model parameters (with associated uncertainty) in order to investigate characteristics of the service-life distribution. Custom software was developed and is included as Supplemental Material to this case study. One part of a responsible approach to the original case was a simulation experiment used to validate the correctness of the software and the behavior of the statistical methodology before using its results in the application, and an example of such an experiment is included here. Because of confidentiality issues that prevent use of the original data, simulated data with characteristics like the manufacturer’s proprietary data are used to illustrate some aspects of our real analyses. Lastly, we also note that, although this case focuses on rare and complete product failure, the statistical methodology provided is directly applicable to more standard warranty data problems involving typically much larger warranty databases where entries are warranty claims (often for repairs) rather than reports of complete failures.},
doi = {},
journal = {Journal of Quality Technology},
number = 2,
volume = 49,
place = {United States},
year = {2017},
month = {4}
}