dc.contributor
Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió
dc.contributor
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor
Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.contributor.author
Fang, Xin
dc.contributor.author
Blesa Izquierdo, Joaquim
dc.contributor.author
Puig Cayuela, Vicenç
dc.identifier
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 .
dc.identifier
https://paperhost.org/proceedings/controls/MED24/files/0149.pdf
dc.identifier
https://hdl.handle.net/2117/420927
dc.identifier
10.1109/MED61351.2024.10566227
dc.description.abstract
© 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.
dc.description.abstract
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.
dc.description.abstract
This work has been co-financed by the Spanish ResearchAgency (AEI) through the projects SaCoAV (ref. MINECOPID2020-114244RB-I00)andL-BEST(PID2020115905RB-C21).
dc.description.abstract
Peer Reviewed
dc.description.abstract
Postprint (published version)
dc.format
application/pdf
dc.publisher
Institute of Electrical and Electronics Engineers (IEEE)
dc.relation
https://ieeexplore.ieee.org/document/10566227
dc.relation
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/
dc.relation
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/
dc.rights
Restricted access - publisher's policy
dc.subject
Àrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.title
Fault prognosis approach using data-driven structurally generated residuals
dc.type
Conference report
