| DOI | Resolve DOI: https://doi.org/10.1007/978-3-642-21822-4_18 |
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| Author | Search for: Yang, Chunsheng1; Search for: Létourneau, Sylvain1 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Book Chapter |
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| Conference | 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2011), June 28-July 1, 2011, Syracuse, NY, USA |
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| Subject | data mining; time-series; reliable patterns; utility; prognostics |
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| Abstract | Data-driven prognostic for system health management represents an emerging and challenging application of data mining. The objective is to develop data-driven prognostic models to predict the likelihood of a component failure and estimate the remaining useful lifetime. Many models developed using techniques from data mining and machine learning can detect the precursors of a failure but sometimes fail to precisely predict time to failure. This paper attempts to address this problem by proposing a novel approach to find reliable patterns for prognostics. A reliable pattern can predict state transitions from current situation to upcoming failures and therefore help better estimate the time to failure. Using techniques from data mining and time-series analysis, we developed a KDD methodology for discovering reliable patterns from multi-stream time-series databases. The techniques have been applied to a real-world application: train prognostics. This paper reports the developed methodology along with preliminary results obtained on prognostics of wheel failures on train. |
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| Publication date | 2011 |
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| Publisher | Springer Berlin Heidelberg |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 20494949 |
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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 | 95667e55-10fe-4ad4-83ba-80ba3613cdc2 |
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| Record created | 2012-08-15 |
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| Record modified | 2020-03-03 |
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