Multi-dimensional failure probability estimation in automotive industry based on censored warranty data

Mark Last, Alexandra Zhmudyak, Hezi Halpert, Sugato Chakrabarty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The warranty datasets available for various car models are characterized by extremely imbalanced classes, where a very low amount of under-warranty vehicles have at least one matching claim ("failure") of a given type. The failure probability estimation becomes even more complex in the presence of censored warranty data, where some of the vehicles have not reached yet the upper limit of the predicted interval. The actual mileage rate of under-warranty vehicles is another source of uncertainty in warranty datasets. In this paper, we present a new, continuous-time methodology for failure probability estimation from multi-dimensional censored datasets in automotive industry.

Original languageEnglish
Title of host publicationSynergies of Soft Computing and Statistics for Intelligent Data Analysis
PublisherSpringer Verlag
Pages507-515
Number of pages9
ISBN (Print)9783642330414
DOIs
StatePublished - 1 Jan 2013
Event6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012 - Konstanz, Germany
Duration: 4 Oct 20126 Oct 2012

Publication series

NameAdvances in Intelligent Systems and Computing
Volume190 AISC
ISSN (Print)2194-5357

Conference

Conference6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012
Country/TerritoryGermany
CityKonstanz
Period4/10/126/10/12

Keywords

  • Automotive Industry
  • censored data
  • multi-dimensional failure prediction
  • warranty data

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science (all)

Fingerprint

Dive into the research topics of 'Multi-dimensional failure probability estimation in automotive industry based on censored warranty data'. Together they form a unique fingerprint.

Cite this