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Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction...
Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition-Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine...
This paper describes a decision support system (DSS) for use in operational decision making with PHM-specific data. Challenges arise from the large amount of different information pieces upon which a decision maker has to act. Conflicting information from on-board and off-board PHM modules, seemingly contradictory and changing requirements from operations as well as maintenance for a multitude of...
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