Adaptive PCA based fault diagnosis scheme in imperial smelting process.
In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently.
School of Physics and Electronics, Central South University, Changsha 410083, China.
Zhikun
Z
Hu
Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, 47057, Germany. Electronic address: zhiwen.chen@uni-due.de.
Zhiwen
Z
Chen
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Weihua
W
Gui
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Bin
B
Jiang
Adaptive principal component analysis; Fault diagnosis; Imperial smelting process monitoring