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oai:recercat.cat:2117/4209272026-02-09T08:19:40Zcom_2072_1033col_2072_452950

Fault prognosis approach using data-driven structurally generated residuals Fang, Xin Blesa Izquierdo, Joaquim 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 © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper presents a fault prognosis approach using data-driven structurally generated residuals. It assumes that a set of residuals generated using structural analysis (SA) and identified using data-driven approach are available. Residuals are used for fault detection purposes activating fault signals when residual values reach anomalous values. In addition, it is possible to predict future faults by means of the detection of anomalous residual deviations. Once an anomalous change in the residual trend has been detected, it is proceed to estimate when this residual deviation will result in a fault detection and therefore which will be the Remaining Useful Life (RUL) time of the system. For this purpose, the future residual evolution is estimated by means of a regressor function. Nominal and interval parameters of regressor function are estimated with available residual data providing nominal and interval values of the RUL of the system. A brushless direct current (BLDC) motor is used as the application case study to illustrate the performance of proposed approach. This work has been co-financed by the Spanish ResearchAgency (AEI) through the projects SaCoAV (ref. MINECOPID2020-114244RB-I00)andL-BEST(PID2020115905RB-C21). Peer Reviewed Postprint (published version) 2024 Conference report Fang, X.; Blesa, J.; Puig, V. Fault prognosis approach using data-driven structurally generated residuals. A: Mediterranean Conference on Control and Automation. "2024 32nd Mediterranean Conference on Control and Automation (MED): June 11-14, 2024, Chania, Crete, Greece". Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 531-536. ISBN 2473-3504. DOI 10.1109/MED61351.2024.10566227 . 2473-3504 https://paperhost.org/proceedings/controls/MED24/files/0149.pdf https://hdl.handle.net/2117/420927 10.1109/MED61351.2024.10566227 eng https://ieeexplore.ieee.org/document/10566227 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/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115905RB-C21/ES/SUPERVISION Y CONTROL TOLERANTE A FALLOS DE INFRAESTRUCTURAS INTELIGENTES BASADO EN APRENDIZAJE AVANZADO Y OPTIMIZACION/ Restricted access - publisher's policy 6 p. application/pdf Institute of Electrical and Electronics Engineers (IEEE)