Automobile maintenance prediction using deep learning with GIS data

Chen, Chong, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Cairano-Gilfedder, Carla and Titmus, Scott 2019. Automobile maintenance prediction using deep learning with GIS data. Procedia CIRP 81 , -. 10.1016/j.procir.2019.03.077

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Abstract

Predictive maintenance is of importance to various industries. Fleet management can be beneficial if the time-between-failures (TBF) of an automobile can be predicted. Conventionally, the prediction models in predictive maintenance are established using historical maintenance data or sensor data. In the era of big data, the availability of data has been significantly increased. This study aims to introduce geographic information systems data into TBF modelling and research their impact on automobile TBF using deep learning. An experimental study based on real-world maintenance data reveals that the performance of deep neural network improved with the help of GIS data.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures
Uncontrolled Keywords:	predictive maintenance; deep learning; GIS; data mining
Publisher:	Elsevier
ISSN:	2212-8271
Date of First Compliant Deposit:	26 March 2019
Date of Acceptance:	11 March 2019
Last Modified:	05 May 2023 20:44
URI:	https://orca.cardiff.ac.uk/id/eprint/120513

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