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oai:uvadoc.uva.es:10324/455992021-06-23T11:21:57Zcom_10324_1168com_10324_931com_10324_894col_10324_1304

Sánchez-Fernández, Alvar Fuente Aparicio, María Jesús de la Sáinz Palmero, Gregorio Ismael 2021-03-09T18:36:26Z 2021-03-09T18:36:26Z 2018 23th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA’2018, Turin, Italia, 2018, p. 800-807 978-1-5386-7107-8 http://uvadoc.uva.es/handle/10324/45599 10.1109/ETFA.2018.8502656 Producción Científica This paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component Analysis model does the fault detection, including delayed data to get a dynamic method. Then, a central node fuses the results of each group, using Bayesian Index Criterion (BIC), to get a global detection outcome. The method was tested on a widely used benchmark and compared with other proposal to check its effectiveness. Este trabajo forma parte del proyecto de investigación: MINECO/FEDER: DPI2015-67341-C2-2-R. 8p application/pdf eng IEEE info:eu-repo/semantics/restrictedAccess IEEE Fault detection Dynamic principal component analysis Decentralized process monitoring Decision fusion Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference 23th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA’2018 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/submittedVersion https://ieeexplore.ieee.org/document/8502656