RT info:eu-repo/semantics/conferenceObject
T1 Decentralized and Dynamic Fault Detection Using PCA and Bayesian Inference
A1 Sánchez-Fernández, Alvar
A1 Fuente Aparicio, María Jesús de la
A1 Sáinz Palmero, Gregorio Ismael
K1 Fault detection
K1 Dynamic principal component analysis
K1 Decentralized process monitoring
K1 Decision fusion
AB 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 dynamicmethod. 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.
PB IEEE
SN 978-1-5386-7107-8
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/45599
UL http://uvadoc.uva.es/handle/10324/45599
LA eng
NO 23th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA’2018, Turin, Italia, 2018, p. 800-807
NO Producción Científica
DS UVaDOC
RD 20-may-2024