Browse > Article
http://dx.doi.org/10.6109/jkiice.2020.24.2.192

Fault Detection Method for Multivariate Process using ICA  

Jung, Seunghwan (Department of Electrical and Computer Engineering, Pusan National University)
Kim, Minseok (Department of Electrical and Computer Engineering, Pusan National University)
Lee, Hansoo (Department of Electrical and Computer Engineering, Pusan National University)
Kim, Jonggeun (Department of Electrical and Computer Engineering, Pusan National University)
Kim, Sungshin (Department of Electrical and Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.2, 2020 , pp. 192-197 More about this Journal
Abstract
Multivariate processes, such as large scale power plants or chemical processes are operated in very hazardous environment, which can lead to significant human and material losses if a fault occurs. On-line monitoring technology, therefore, is essential to detect system faults. In this paper, the ICA-based fault detection method is conducted using three different multivariate process data. Fault detection procedure based on ICA is divided into off-line and on-line processes. The off-line process determines a threshold for fault detection by using the obtained dataset when the system is normal. And the on-line process computes statistics of query vectors measured in real-time. The fault is detected by comparing computed statistics and previously defined threshold. For comparison, the PCA-based fault detection method is also implemented in this paper. Experimental results show that the ICA-based fault detection method detects the system faults earlier and better than the PCA-based method.
Keywords
fault detection; process monitoring; independent component analysis; multivariate process;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 J. S. Oh, "Straight Line Detection Using PCA and Hough transform," Journal of the Korea Institute of Information and Communication Engineering, vol. 22, no. 7, pp. 227-232, Feb. 2018.
2 J. M. Lee, C. K. Yoo, and I. B. Lee, "Statistical monitoring of dynamic processes based on dynamic independent component analysis," Chemical Engineering Science, vol. 59, pp. 2995-3006, Jul. 2004.   DOI
3 W. Ku, R. H. Storer, and C. Georgakis, "Disturbance detection and isolation by dynamic principal component analysis," Chemometrics and Intelligent Laboratory System, vol. 30, pp. 179-196, 1995.   DOI
4 M. Kano, S. Hasebe, I. Hashimoto, and H. Ohno, "A new multivariate statistical process monitoring method using principal component analysis," Computer and Chemical Engineering, vol. 25, pp. 1103-1113, 2001.   DOI
5 M. Z. Sheriff, M. Mansouri, M. N. Karim, H. Nounou, and M. Nounou, "Fault detection using multiscale PCA-based moving window GLRT," Journal of Process Control, vol. 54, pp. 47-64, 2017.   DOI
6 F. Zhou, J. H. Park, and Y. Liu, "Differential feature based hierarchical PCA fault detection method for dynamic fault," Neurocomputing, vol. 202, pp. 27-35, 2016.   DOI
7 M. Zvokelj, S. Zupan, and I. Prebil, "EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis," Journal of Sound and Vibration, vol. 370, pp. 394-423, 2016.   DOI
8 S. Jung, M. Kim, J. Jang, J. Yoo, and S. Kim, "Fault detection and Diagnosis for High Pressure Feedwater Heater using Principal Component Analysis," Journal of Korean Institute of Intelligent Systems, vol. 27, no, 2, Nov. 2017.
9 S. Jung, B. Kim, J. Jang, J. Yoo, and S. Kim, "Fault detection method for high pressure feedwater heater in thermal power plant using independent component analysis," Proceedings of KIIS Autumn Conf, vol. 28, no, 3, Nov. 2018.
10 A. Hyvarinen, "Fast and robust fixed-point algorithm for independent component analysis," IEEE Trans. Neural Networks, vol. 10, no. 3, pp. 626-634, 1999.   DOI
11 A. D. Back, and A. S. Weigend, "A first application of independent component analysis to extracting structure from stock returns," International Journal of Neural Systems, vol. 8, no. 4, pp. 473-484, 1997.   DOI
12 A. Hyvarinen, "Survey on independent component analysis," Neural computing surveys, vol. 2, no. 4, pp. 94-128, 1999. Control, vol. 68, pp. 129-144, 1999.