Wu, Shaomin, Wu, Di, Peng, Rui (2020) Machine learning approaches in reliability and maintenance: classifications of recent literature. In: Xing, Liudong and Tian, Zhigang and Peng, Rui and Zuo, Ming J., eds. 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM). . IEEE ISBN 978-1-72817-103-6. E-ISBN 978-1-72817-102-9. (doi:10.1109/APARM49247.2020.9209392) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:83222)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication) | |
| Official URL: https://doi.org/10.1109/APARM49247.2020.9209392 |
Abstract
Reliability and maintenance (R&M) engineering is conventionally notorious for a lack of sufficient failure data to develop robust statistical models. The increasing miniaturization of data collection devices such as wireless sensors has provided a promising infrastructure for gathering information about parameters of the physical systems, which enable practitioners and researchers to apply machine learning (ML) algorithms to improve the efficiency of R&M analysis. The number of published papers on ML in R&M is enormous, this paper will therefore categorizes those papers that were published between 2017 to 16/May/2020, that are written in English, that have received a top 5% number of citations in the year published, and that use support vector methods, random forests, and cluster analysis.
| Item Type: | Conference or workshop item (Paper) |
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| DOI/Identification number: | 10.1109/APARM49247.2020.9209392 |
| Projects: | Smart Data Analytics for Business and Local Government |
| Uncontrolled keywords: | Machine learning; Reliability estimation; Condition monitoring; Fault diagnosis; Maintenance policy |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
| Funders: | Economic and Social Research Council (https://ror.org/03n0ht308) |
| Depositing User: | Shaomin Wu |
| Date Deposited: | 01 Oct 2020 16:13 UTC |
| Last Modified: | 04 Mar 2024 19:15 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/83222 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Wu, Shaomin.
| Creator's ORCID: |
 https://orcid.org/0000-0001-9786-3213
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