| DOI | Resolve DOI: https://doi.org/10.1109/APARM49247.2020.9209574 |
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| Author | Search for: Wang, Teng; Search for: Liu, Zheng; Search for: Zhao, Xiaoli; Search for: Liao, Min1; Search for: Mrad, Nezih1 |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Format | Text, Article |
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| Conference | 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), August 20-23, 2020, Vancouver, BC, Canada |
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| Subject | remaining life prediction; reliability prediction; Bayesian theory; grid-sampling |
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| Abstract | One of the key targets of prognostic and health management is to predict the remaining useful life (RUL) and reliability of equipment. This technique can not only guarantee the reliability of the inspected component, but also reduce the maintenance cost during their lifetime. This paper proposes a RUL and reliability prediction method based on Bayesian theory and grid sampling for a steel structure. Specifically, the accumulated damage is assumed following the Paris-Erdogan model, so that Bayesian theory can be adopted to infer the unknown parameters within this model. Then a grid-sampling strategy is introduced to calculate the so-called "peak" and "profile" which are used for RUL and reliability prediction, respectively. The proposed method is adopted to the RUL and reliability prediction of a set of steel tension experimental specimens, and is benchmarked with a similar study reported recently. The result shows the superiority of this method, which can be effective even under insufficient prior knowledge. |
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| Publication date | 2020-09-30 |
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| Publisher | IEEE |
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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 | 654f9436-cc26-4144-ba24-23151048f2dd |
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| Record created | 2021-12-23 |
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| Record modified | 2021-12-23 |
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