RT info:eu-repo/semantics/conferenceObject
T1 Decentralized DPCA Model for Large-Scale Processes Monitoring
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 monitoring
K1 Regression
K1 Clustering
AB Monitoring large-scale processes is a crucial task to ensure the safety and reliability of the plants. This paper proposes an approach for decentralized fault detection in largescale processes. The measured variables of the plant are divided into multiple and possibly overlapping blocks using different techniques based on data. Local monitoring methods are applied in each block using DPCA (Dynamic Principal Component Analysis) model. The local results are then fused by the Bayesian inference strategy. This paper also compares different techniques to decompose the plant looking for the best strategy fromthe point of view of the fault detection results. The proposed method was applied to the widely used benchmark Tennessee Eastman Process, showing its effectiveness when compared with a centralized method and another decentralized technique.
PB IEEE
SN 978-1-7281-0303-7
YR 2019
FD 2019
LK http://uvadoc.uva.es/handle/10324/45604
UL http://uvadoc.uva.es/handle/10324/45604
LA eng
NO 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA’2019, Zaragoza, España, 2019
NO Producción Científica
DS UVaDOC
RD 22-nov-2024