Automobile maintenance modelling using gcForest

Chen, Chong, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Di Cairano-Gilfedder, Carla and Titmus, Scott 2020. Automobile maintenance modelling using gcForest. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual, 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, pp. 600-605. 10.1109/CASE48305.2020.9216745

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Abstract

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery
Publisher:	IEEE
ISBN:	9781728169040
Date of First Compliant Deposit:	4 June 2020
Date of Acceptance:	30 May 2020
Last Modified:	05 Nov 2022 03:17
URI:	https://orca.cardiff.ac.uk/id/eprint/132145

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