| DOI | Resolve DOI: https://doi.org/10.1007/s10489-017-1083-0 |
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| Author | Search for: Yang, Chunsheng1; Search for: Lou, Qingfeng; Search for: Liu, Jie; Search for: Yang, Yubin; Search for: Cheng, Qiangqiang |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | machine learning; predictive maintenance; particle filtering; Time to Failure (TTF); modeling |
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| Abstract | One of core technologies for prognostics is to predict failures before they occur and estimate time to failure (TTF) by using built-in predictive models. The predictive model could be either physics-based model or machine learning-based model. Machine learning-based predictive modeling is an emerging application of machine learning to machinery maintenance. Accurate TTF estimation could help performing predictive action “just-in-time”. However, the developed predictive models sometimes fail to provide a precise TTF estimate. To address this issue, we propose a Particle Filtering (PF)-based method to estimate TTF. After introducing the PF-based algorithm, we present the implementation along with the experimental results obtained from a case study of Auxiliary Power Unit (APU) prognostics. To our best knowledge, this is the first application of PF-based method to APU prognostic. The results demonstrated that the PF-based method is useful for estimating TTF for predictive maintenance and it greatly improved TTF estimation precision for APU prognostics. |
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| Publication date | 2017-12-06 |
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| Publisher | Springer |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8f99be20-8858-469e-ad63-f934c772b105 |
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| Record created | 2019-04-26 |
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| Record modified | 2020-03-16 |
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