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

Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference Sánchez-Fernández, Alvar Fuente Aparicio, María Jesús de la Sáinz Palmero, Gregorio Ismael 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. 2021-03-09T18:36:26Z 2021-03-09T18:36:26Z 2018 info:eu-repo/semantics/conferenceObject 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 eng https://ieeexplore.ieee.org/document/8502656 info:eu-repo/semantics/restrictedAccess IEEE IEEE