RT info:eu-repo/semantics/article
T1 Fault detection based on time series modeling and multivariate statistical process control
A1 Sánchez-Fernández, Alvar
A1 Baldán, Francisco Javier
A1 Sáinz Palmero, Gregorio Ismael
A1 Benítez, José Manuel
A1 Fuente Aparicio, María Jesús de la
K1 Fault detection
K1 Dynamic feature selection
K1 Time-series modelling
K1 Statistical process control charts
AB Monitoring complex industrial plants is a very important task in order to ensure the management, reliability, safety and maintenance of the desired product quality. Early detection of abnormal events allows actions to prevent more serious consequences, improve the system's performance and reduce manufacturing costs. In this work, a new methodology for fault detection is introduced, based on time series models and statistical process control (MSPC). The proposal explicitly accounts for both dynamic and non-linearity properties of the system. A dynamic feature selection is carried out to interpret the dynamic relations by characterizing the auto- and crosscorrelations for every variable. After that, a time-series based model framework is used to obtain and validate the best descriptive model of the plant (either linear o non-linear). Fault detection is based on finding anomalies in the temporal residual signals obtained from the models by univariate and multivariate statistical process control charts. Finally, the performance of the method is validated on two benchmarks, a wastewater treatment plant and the Tennessee Eastman Plant. A comparison with other classical methods clearly demonstrates the over performance and feasibility of the proposed monitoring scheme.
PB Elsevier
SN 0169-7439
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/45594
UL http://uvadoc.uva.es/handle/10324/45594
LA eng
NO Chemometrics and Intelligent Laboratory Systems, Noviembre 2018, vol. 182, p. 57–69
NO Producción Científica
DS UVaDOC
RD 05-may-2024