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

Mark Last; Alexandra Zhmudyak; Hezi Halpert; Sugato Chakrabarty

doi:10.1007/978-3-642-33042-1_54

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

Mark Last, Alexandra Zhmudyak, Hezi Halpert, Sugato Chakrabarty

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	American English
Title of host publication	Synergies of Soft Computing and Statistics for Intelligent Data Analysis
Publisher	Springer Verlag
Pages	507-515
Number of pages	9
ISBN (Print)	9783642330414
DOIs	https://doi.org/10.1007/978-3-642-33042-1_54
State	Published - 1 Jan 2013
Event	6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012 - Konstanz, Germany Duration: 4 Oct 2012 → 6 Oct 2012

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	190 AISC

Conference

Conference	6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012
Country/Territory	Germany
City	Konstanz
Period	4/10/12 → 6/10/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Automotive Industry
censored data
multi-dimensional failure prediction
warranty data

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-642-33042-1_54

Cite this

Last, M., Zhmudyak, A., Halpert, H., & Chakrabarty, S. (2013). Multi-dimensional failure probability estimation in automotive industry based on censored warranty data. In Synergies of Soft Computing and Statistics for Intelligent Data Analysis (pp. 507-515). (Advances in Intelligent Systems and Computing; Vol. 190 AISC). Springer Verlag. https://doi.org/10.1007/978-3-642-33042-1_54

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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.",

keywords = "Automotive Industry, censored data, multi-dimensional failure prediction, warranty data",

author = "Mark Last and Alexandra Zhmudyak and Hezi Halpert and Sugato Chakrabarty",

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Last, M, Zhmudyak, A, Halpert, H & Chakrabarty, S 2013, Multi-dimensional failure probability estimation in automotive industry based on censored warranty data. in Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol. 190 AISC, Springer Verlag, pp. 507-515, 6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012, Konstanz, Germany, 4/10/12. https://doi.org/10.1007/978-3-642-33042-1_54

Multi-dimensional failure probability estimation in automotive industry based on censored warranty data. / Last, Mark; Zhmudyak, Alexandra; Halpert, Hezi et al.
Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Springer Verlag, 2013. p. 507-515 (Advances in Intelligent Systems and Computing; Vol. 190 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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KW - censored data

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KW - warranty data

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PB - Springer Verlag

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

Abstract

Publication series

Conference

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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