Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis

IEEE Transactions on Control Systems Technology

Volumn 16, Issue 1, 2008, Pages 122-129

  Wang, Xun ^a   Kruger, Uwe ^a   Irwin, George W ^a   McCullough, Geoff ^a   McDowell, Neil ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Automotive engines; Fault detection and diagnosis (FDD); Nonlinear statistical process control; Normal distribution; Statistical inference; Statistical local approach

Indexed keywords

AUTOMOTIVE ENGINEERING; DIESEL ENGINES; FAILURE ANALYSIS; FAULT DETECTION; NONLINEAR CONTROL SYSTEMS; NORMAL DISTRIBUTION;

AUTOMOTIVE ENGINES; STATISTICAL INFERENCE;

STATISTICAL PROCESS CONTROL;

EID: 37749003880     PISSN: 10636536     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCST.2007.899744     Document Type: Article

References (16)

1. J. J. Gertler, M. Costin, X. Fang, R. Hira, Z. Kowalczuk, and Q. Luo, "Model-based on-board fault detection and diagnosis for automotive engines," Control Eng. Practice, vol. 1, no. 1, pp. 3-17, 1993.
2. M. Nyberg, "Model-based diagnosis of an automotive engine using several types of fault modes," IEEE Trans. Control Syst. Technol., vol. 10, no. 5, pp. 679-689, Sep. 2002.
3. A. K. Sood, C. B. Friedlander, and A. A. Fahs, "Engine fault analysis: Part I - statistical methods," IEEE Trans. Ind. Electron., vol. 32, no. 4, pp. 294-300, Jul./Aug. 1985.
4. S. Yoon and J. F. MacGregor, "Statistical causal model-based approaches to fault detection and isolation," AIChE J., vol. 46, no. 9, pp. 1813-1824, 2000.
5. M. A. Kramer, "Autoassociative neural networks," Comput. Chem. Eng. vol. 16, no. 4, pp. 313-328, 1992.
6. F. Jia, E. B. Martin, and A. J. Morris, "Non-linear principal component analysis for process fault detection," Comput. Chem. Eng., vol. 22, pp. 8851-8854, 1998.
7. T. Hastie and W. Stuetzle, "Principal curves," J. Amer. Stat. Assoc., vol. 84, no. 406, pp. 502-517, 1989.
8. D. Dong and T. J. McAvoy, "Non-linear principal component analysis-based on principal curves and neural networks," Comput. Chem. Eng., vol. 20, no. 1, pp. 65-78, 1996.
9. D. Antory, U. Kruger, G. W. Irwin, and G. McCullough, "Fault diagnosis in internal combustion engines using nonlinear multivariate statistics," Proc. IME - Part I: J. Syst. Control Eng., vol. 219, no. 4, pp. 243-258, 2005.
10. B. W. Silverman, "Density Estimation in Statistics and Data Analysis," in Monograph on Statistics and Data Analysis 26. London, U.K.: Chapman Hall, 1986.
11. M. Basseville, "On-board component fault detection and isolation using the statistical local approach," Automatica, vol. 34, no. 11, pp. 1391-1415, 1998.
12. Q. Zhang, M. Basseville, and A. Benveniste, "Early warning of slight changes in (systems and plants with application to condition based maintenance," Automatica, vol. 30, no. 1, pp. 95-114, 1994.
13. P. R. C. Nelson, P. A. Taylor, and J. F. MacGregor, "Missing data methods in PCA and PLS: Score calculations with incomplete observations," Chem. Intell. Lab. Syst., vol. 35, no. 1, pp. 45-65, 1996.
14. P. Nomikos and J. F. MacGregor, "Multivariate SPG charts for monitoring batch processes," Technometries, vol. 37, no. 1, pp. 41-59, 1995.
15. D. Lieftucht, U. Kruger, L. Xie, T. Littler, Q. Chen, and S.-Q. Wang, "Statistical monitoring of dynamic multivariate processes - Part 2. Identifying fault magnitude and signature," Ind. Eng. Chem. Res., vol. 45, no. 5, pp. 1677-1688, 2006.
16. X. Wang, U. Kruger, G. W. Irwin, N. McDowell, and G. McCullough, "Nonlinear PCA for process monitoring using the local approach," in Proc. 6th IFAC Symp. Fault Detection, Supervision Safety Tech. Processes 2006, pp. 115-120.