2026-04-13T15:16:58Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3842412026-02-07T11:00:59Zcom_2072_1033col_2072_452950

A set-based uncertainty quantification of evolving fuzzy models for data-driven prognostics Khoury, Boutrous Bessa, Iury Nejjari Akhi-Elarab, Fatiha Puig Cayuela, Vicenç Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control Àrees temàtiques de la UPC::Informàtica::Automàtica i control Predictive control System failures (Engineering) Uncertainty quantification Data driven prognostics Setbased propagation Interval arithmetic Evolving ellipsoidal fuzzy information granules Control predictiu Anàlisi de fallades (Enginyeria) Recent years have seen a great deal of innovation in the field of systems prognostics and health management. However, even with these advancements, some pertinent issues related with uncertainty in remaining useful life predictions are still open for investigation. One such area of interest is on how to account for the distribution of these predictions such that all uncertainty sources are duly captured and represented. Practically, these uncertainty quantification procedures must be computationally feasible for real-life deployment and reflect real-life situations devoid of strong assumptions. This article thus, proposes a data-based prognostics technique that uses a set-based quantification of uncertainty based on the set-membership paradigm, the interval predictor approach. The methodology is applied in the framework of the Evolving Ellipsoidal Fuzzy Information Granule which has recently proven its potency in prognostics applications. As a case study, the method is tested on the prognostics of insulated bipolar transistors utilising an accelerated aging IGBT dataset from the NASA Ames Research Center. Peer Reviewed Postprint (author's final draft) 2022 Conference report Khoury, B. [et al.]. A set-based uncertainty quantification of evolving fuzzy models for data-driven prognostics. A: International Conference on Diagnostics of Processes and Systems. "Intelligent and safe computer systems in control and diagnostics". Berlín: Springer, 2022, p. 293-304. ISBN 978-3-031-16159-9. DOI 10.1007/978-3-031-16159-9_24. 978-3-031-16159-9 https://hdl.handle.net/2117/384241 10.1007/978-3-031-16159-9_24 eng https://link.springer.com/book/10.1007/978-3-031-16159-9 IU16-011733 CETPD Smart info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114244RB-I00/ES/COORDINACION SEGURA DE VEHICULOS AUTONOMOS/ http://creativecommons.org/licenses/by-nc-nd/4.0/ Restricted access - publisher's policy Attribution-NonCommercial-NoDerivatives 4.0 International 12 p. application/pdf Springer