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dc.contributor.authorAl Mohamad, Ahmad
dc.contributor.authorHoblos, Ghaleb
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2021-03-01T10:13:09Z
dc.date.issued2020-08
dc.identifier.citationAl Mohamad, A.; Hoblos, G.; Puig, V. A hybrid system-level prognostics approach with online RUL forecasting for electronics-rich systems with unknown degradation behaviors. "Microelectronics reliability", Agost 2020, vol. 111, p. 113676:1-113676:10.
dc.identifier.issn0026-2714
dc.identifier.otherhttps://www.researchgate.net/publication/342762921_A_hybrid_system-level_prognostics_approach_with_online_RUL_forecasting_for_electronics-rich_systems_with_unknown_degradation_behaviors
dc.identifier.urihttp://hdl.handle.net/2117/340623
dc.description.abstractThis paper proposes a system-level prognostic approach for power electronic systems with slow degradation profiles. Although a model-based approach has been adopted to deal with such multivariable dynamical systems with degradation properties, the forecasting of the Remaining Useful Life (RUL) is independent of prior knowledge of degradation profiles. Thus, this proposition is mainly based on the estimation of the degraded parameters. A robust and well-known technique, the Adaptive Joint Extended Kalman Filter (AJEKF), has been used in previous publications for degradation estimation. Consequently, a deep comprehension of the fault mechanisms of the critical electronic components such as Electrolytic Capacitors (ECaps) and power switching devices such as MOSFETs is needed to define their fault precursors and their degradation behaviors for analytical modeling. The developed forecasting methodology highlights the importance of the historical degradation data in the modeling and estimation stages. The main goal is to increase the reliability of the Prognostics and Health Management (PHM). Thus, this technique has been fully applied to a DC-DC converter used in electric vehicles to forecast its RUL on system-level from component-level basis and the simulation results are then presented.
dc.description.sponsorshipThis work is co-funded by European Union and Normandy Region. Europe is involved in Normandy through the European Funds for Regional Development.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència
dc.subject.lcshPredictive control
dc.subject.lcshKalman filtering
dc.subject.otherModel-based prognostics
dc.subject.otherParameter estimation
dc.subject.otherPower electronics
dc.subject.otherPrediction algorithms
dc.subject.otherPrognostics and health management
dc.subject.otherReliability assessment
dc.subject.otherRemaining useful life
dc.titleA hybrid system-level prognostics approach with online RUL forecasting for electronics-rich systems with unknown degradation behaviors
dc.typeArticle
dc.subject.lemacControl predictiu
dc.subject.lemacKalman, Filtratge de
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.microrel.2020.113676
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0026271419312399
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac28894467
dc.description.versionPostprint (author's final draft)
dc.date.lift2022-08
local.citation.authorAl Mohamad, A.; Hoblos, G.; Puig, V.
local.citation.publicationNameMicroelectronics reliability
local.citation.volume111
local.citation.startingPage113676:1
local.citation.endingPage113676:10


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