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A self-organizing m...
A self-organizing map and a normalizing multi-layer perceptron approach to baselining in prognostics under dynamic regimes
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- Baptista, Marcia Lourenco (författare)
- Air Transport and Operations, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands
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- Henriques, Elsa M.P. (författare)
- LAETA, IDMEC, Instituto Superior Tecnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
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- Goebel, Kai (författare)
- Luleå tekniska universitet,Drift, underhåll och akustik,Palo Alto Research Center, Palo Alto, CA, 94304, United States
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Air Transport and Operations, Faculty of Aerospace Engineering, Delft University of Technology, Delft, Netherlands LAETA, IDMEC, Instituto Superior Tecnico, University of Lisbon, Av Rovisco Pais 1, 1049-001 Lisboa, Portugal (creator_code:org_t)
- Elsevier, 2021
- 2021
- Engelska.
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 456, s. 268-287
- Relaterad länk:
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https://doi.org/10.1...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- When the influence of changing operational and environmental conditions is not factored out, it can be dificult to observe a clear deterioration path. This can significantly affect the task of prognostics and other analytic operations. To address this issue, it is necessary to baseline the data, typically by first finding the operating regimes and then normalizing the data within each regime. In this paper, we propose the use of machine learning techniques to perform baselining. A self-organizing map identifies the regimes, and a multi-layer perceptron normalizes the data based on the detected regimes. Tests are performed on the C-MAPSS data. The approach is capable of producing similar results to classical methods without the need to specify in advance the number of regimes and the explicit computation of the statistical properties of a hold-out dataset. Importantly, the techniques can be integrated into a deep learning system to perform prognostics in a single pass.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Self-organizing map
- Normalizing multi-layer perceptron
- Prognostics
- Baselining
- C-MAPSS datasets
- Drift och underhållsteknik
- Operation and Maintenance
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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