A data-model-fusion prognostic framework for dynamic system state forecasting

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DOIResolve DOI: http://doi.org/10.1016/j.engappai.2012.02.015
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TypeArticle
Journal titleEngineering Applications of Artificial Intelligence
ISSN0952-1976
Volume25
Issue4
Pages814823; # of pages: 10
SubjectNonlinear prediction; Fault diagnosis; Failure prognostics; Neural networks; Neural fuzzy systems; Remaining useful life prediction
AbstractA novel data-model-fusion prognostic framework is developed in this paper to improve the accuracy of system state long-horizon forecasting. This framework strategically integrates the strengths of the data-driven prognostic method and the model-based particle filtering approach in system state prediction while alleviating their limitations. In the proposed methodology, particle filtering is applied for system state estimation in parallel with parameter identification of the prediction model (with unknown parameters) based on Bayesian learning. Simultaneously, a data-driven predictor is employed to learn the system degradation pattern from history data so as to predict system evolution (or future measurements). An innovative feature of the proposed fusion prognostic framework is that the predicted measurements (with uncertainties) from the data-driven predictor will be properly managed and utilized by the particle filtering to further update the prediction model parameters, thereby enabling markedly better prognosis as well as improved forecasting transparency. As an application example, the developed fusion prognostic framework is employed to predict the remaining useful life of lithium ion batteries through electrochemical impedance spectroscopy tests. The investigation results demonstrate that the proposed fusion prognostic framework is an effective forecasting tool that can integrate the strengths of both the data-driven method and the particle filtering approach to achieve more accurate state forecasting.
Publication date
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
IdentifierS0952197612000528
NPARC number21268747
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Record identifier99dd5e45-39fc-4d73-bc67-9c558457de54
Record created2013-11-12
Record modified2016-05-09
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