Dynamic independent component analysis approach for fault detection and diagnosis

Expert Systems with Applications

Volumn 37, Issue 12, 2010, Pages 8606-8617

  Stefatos, George ^a   Hamza, A Ben ^b  

^a PRATT AND WHITNEY CANADA   (Canada)

^b Concordia University ^*  (Canada)

Author keywords

Dynamic independent component analysis; Fault analysis and diagnostics; Fault detection; Multivariate process control

Indexed keywords

INDEPENDENT COMPONENT ANALYSIS; MULTIVARIANT ANALYSIS;

DETECTION METHODS; DYNAMIC INDEPENDENT COMPONENT ANALYSIS; FAULT ANALYSIS; FAULT DETECTION AND DIAGNOSIS; FAULT DETECTION AND DIAGNOSIS METHODS; MULTIVARIATE PROCESS CONTROL; STATISTICAL MONITORING; TENNESSEE EASTMAN CHALLENGE;

FAULT DETECTION;

EID: 77957832720     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.06.101     Document Type: Article

References (27)

1. Al-Bazzaz, H., & Wang, X. Z. (2004). New statistical process control chart for batch operations based on independent component analysis. Industrial & Engineering Chemistry Research, 43(21), 6731-6741.
2. Cardoso, J. F., & Soulomica, A. (1993). Blind beamforming for non-Gaussian signals. IEE Proceedings Radar and Signal Processing, 140, 362-370.
3. Chen, K. H., Boning, D. S., & Welch, R. E. (2001). Multivariate statistical process control and signature analysis using eigenfactor detection methods. In Proceedings of symposium on the interface of computer science and statistics, Costa Mesa, CA.
4. Chen, J., & Liao, C. M. (2002). Dynamic process fault monitoring based on neural network and PCA. Journal of Process Control, 12(2), 277-289. (Pubitemid 33110475)
5. Chiang, L. H., Pell, R. J., & Seasholtz, M. B. (2003). Exploring process data with the use of robust outlier detection algorithms. Journal of Process Control, 13(5), 437-449.
6. Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001). Fault detection and diagnosis in industrial systems. Springer.
7. Detroja, K. P., Gudi, R. D., & Patwardhan, S. C. (2006). A possibilistic clustering approach to novel fault detection and isolation. Journal of Process Control, 16(10), 1055-1073.
8. Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering, 17, 245-255.
9. Ge, Z., & Song, Z. (2007). Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Industrial & Engineering Chemistry Research, 46, 2054-2063.
10. Golshan, M., Pishvaie, M. R., & Boozarjomehry, R. B. (2007). Stochastic and global real time optimization of Tennessee Eastman challenge problem. Engineering Applications of Artificial Intelligence.
11. Hyvarinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13, 411-430. (Pubitemid 30447427)
12. Jolliffe, I. T. (1986). Principal component analysis. New York: Springer.
13. Kano, M., Tanaka, S., Hasebe, S., Hashimoto, I., & Ohno, H. (2002). Combination of independent component analysis and principal component analysis for multivariate statistical process control. In Proceedings of international symposium on design, operation and control of chemical plants (pp. 319-324).
14. Ku, W., Storer, R. H., & Georgakis, C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 30, 179-196.
15. Larsson, T., & Skogestad, S. (2000). Plantwide control: A review and a new design procedure. Modeling, Identification and Control, 21, 209-240. (Pubitemid 32869740)
16. Lee, J. M., Yoo, C., & Lee, I. B. (2004). Statistical process monitoring with independent component anlaysis. Journal of Process Control, 14(5), 467-485.
17. Liang, J., & Wang, N. (2003). Fault diagnosis in industrial reheating furnace using principal component analysis. In Proceedings of IEEE conference on neural networks and signal processing (Vol. 2, pp. 1615-1618).
18. Li, W., & Qin, S. J. (2001). Consistent dynamic PCA based on errors-in-variables subspace identification. Journal of Process Control, 11(6), 661-678.
19. Maurya, M. R., Rengaswamy, R., & Venkatasubramanian, V. (2007). Fault diagnosis using dynamic trend analysis: A review and recent developments. Engineering Applications of Artificial Intelligence, 20(2), 133-146.
20. Mina, J., & Verde, C. (2005). Fault detection using dynamic principal component analysis by average estimation. In Proceedings of international conference on electrical and electronics engineering (pp. 374-377).
21. Montgomery, D. C. (2005). Introduction to statistical quality control. John Wiley & Sons.
22. Singhal, A., & Seborg, D. E. (2006). Evaluation of a pattern matching method for the Tennessee Eastman challenge process. Journal of Process Control, 16(6), 601-613.
23. Uraikul, V., Chan, C. W., & Tontiwachwuthikul, P. (2007). Artificial intelligence for monitoring and supervisory control of process systems. Engineering Applications of Artificial Intelligence, 20(2), 115-131.
24. Wand, M. P., & Jones, M. C. (1995). Kernel smoothing. London: Chapman and Hall.
25. Yang, K., & Trewn, J. (2004). Multivariate statistical methods in quality management. Mc Graw Hill Professional.
26. Zaki, O., Brown, K., Fletcher, J., & Lane, D. (2007). Detecting faults in heterogeneous and dynamic systems using DSP and an agent-based architecture. Engineering Applications of Artificial Intelligence, 20(8), 1112-1124.
27. Zhang, H., Tangirala, A. K., & Shah, S. L. (1999). Dynamic process monitoring using multiscale PCA. In Proceedings of IEEE Canadian conference on electrical and computer engineering (Vol. 3, pp. 1579-1584).