Reliability analysis for automobile engines: conditional inference trees

Wang, Shixuan, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Cairano-Gilfedder, Carla Di, Titmus, Scott, Naim, Mohamed ORCID: https://orcid.org/0000-0003-3361-9400 and Syntetos, Argyrios ORCID: https://orcid.org/0000-0003-4639-0756 2018. Reliability analysis for automobile engines: conditional inference trees. Procedia CIRP 72 , pp. 1392-1397. 10.1016/j.procir.2018.03.065

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (550kB) | Preview

Abstract

The reliability model with covariates for machinery parts has been extensively studied by the proportional hazards model (PHM) and its variants. However, it is not straightforward to provide business recommendations based on the results of the PHM. We use a novel method, namely the Conditional Inference Tree, to conduct the reliability analysis for the automobile engines data, provided by a UK fleet company. We find that the reliability of automobile engines is significantly related to the vehicle age, early failure, and repair history. Our tree-structured model can be easily interpreted, and tangible business recommendations are provided for the fleet management and maintenance.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier
ISSN:	2212-8271
Date of First Compliant Deposit:	1 July 2018
Date of Acceptance:	27 June 2018
Last Modified:	06 May 2023 16:45
URI:	https://orca.cardiff.ac.uk/id/eprint/112903

Citation Data

Cited 9 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item