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Soft Computing Applications to Prognostics and Health Management (PHM): Leveraging Field Data and Domain Knowledge

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Computational and Ambient Intelligence (IWANN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4507))

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Abstract

Assets Prognostics and Health Management (PHM) is a promising application area for Soft Computing (SC). To better understand PHM requirements, we introduce a decision-making framework in which we analyze PHM decisional tasks. This framework is the cross product of the decision’s time horizon and the domain knowledge used by SC models. Within such a framework, we analyze the progression from simple to annotated lexicon, morphology, syntax, semantics, and pragmatics. We use this metaphor to monitor the leverage of domain knowledge in SC to perform anomaly detection, anomaly identification, failure mode analysis (diagnostics), estimation of remaining useful life (prognostics), on-board control, and off board logistics actions. We illustrate a case study in anomaly detection, which is solved by the construction and fusion of an ensemble of diverse detectors, each of which is based on different SC technologies.

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Francisco Sandoval Alberto Prieto Joan Cabestany Manuel Graña

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© 2007 Springer-Verlag Berlin Heidelberg

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Bonissone, P.P., Iyer, N. (2007). Soft Computing Applications to Prognostics and Health Management (PHM): Leveraging Field Data and Domain Knowledge. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_112

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  • DOI: https://doi.org/10.1007/978-3-540-73007-1_112

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73006-4

  • Online ISBN: 978-3-540-73007-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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