Reliability analysis using deep learning

Chen, Chong, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Wang, Shixuan, Di Cairano-Gilfedder, Carla, Titmus, Scott and Syntetos, Aris ORCID: https://orcid.org/0000-0003-4639-0756 2018. Reliability analysis using deep learning. Presented at: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Quebec City, Canada, 26-29 August 2018. ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 1B: 38th Computers and Information in Engineering Conference Quebec City, Quebec, Canada, August 26–29, 2018. ASME, V01BT02A040. 10.1115/DETC2018-86172

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Abstract

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and non-linearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics) Computer Science & Informatics Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures
Publisher:	ASME
ISBN:	9780791851739
Last Modified:	22 Sep 2023 06:26
URI:	https://orca.cardiff.ac.uk/id/eprint/114104

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