Soft computing applications to prognostics and health management (PHM): leveraging field data and domain knowledge

Authors:
Piero P. Bonissone;Naresh Iyer
Affiliations:
General Electric Global Research, Niskayuna, NY;General Electric Global Research, Niskayuna, NY
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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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.