Generalized Hebbian Algorithm for Fault Detection of CSTR Model
Conference Paper
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
2016 IEEE. By applying Generalized Hebbian Algorithm (GHA), this work deals with the problem of on-line process monitoring for a continuously stirred tank reactor (CSTR) model using Principal Component Analysis (PCA) method. Diverse studies have shown the efficiency of PCA for fault detection. However, this method for a large number of samples, becomes difficult to directly solve the eigenvalue problem especially with a large number of samples, which make it not suitable in the cases of on-line process monitoring. In this paper, Iterated PCA have been proposed to alleviate the impact of this problem. This method uses GHA for optimizing the memory efficiency. The simulation results show the effectiveness of the IPCA method in terms of fault detection accuracies, false alarm rates for detection of single as well as multiple sensor faults through its two charts Q and Hotelling T2 statistics.
name of conference
2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)