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A self-organizing map and a normalizing multi-layer perceptron approach to baselining in prognostics under dynamic regimes
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.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Palo Alto Research Center, Palo Alto, CA, 94304, United States.ORCID iD: 0000-0002-0240-0943
2021 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 456, p. 268-287Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 456, p. 268-287
Keywords [en]
Self-organizing map, Normalizing multi-layer perceptron, Prognostics, Baselining, C-MAPSS datasets
National Category
Computer Sciences
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-84616DOI: 10.1016/j.neucom.2021.05.031ISI: 000684998100007Scopus ID: 2-s2.0-85107983909OAI: oai:DiVA.org:ltu-84616DiVA, id: diva2:1556819
Note

Validerad;2021;Nivå 2;2021-06-23 (beamah)

Available from: 2021-05-24 Created: 2021-05-24 Last updated: 2021-08-30Bibliographically approved

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Goebel, Kai

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