Data-driven adaptive observer for fault diagnosis

Mathematical Problems in Engineering

Volumn 2012, Issue , 2012, Pages

  Yin, Shen ^a   Yang, Xuebo ^a   Karimi, Hamid Reza ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF AGDER   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE OBSERVER; ADAPTIVE RESIDUAL GENERATORS; ADAPTIVE TECHNIQUE; ANALYTICAL PROCESS; DATA-DRIVEN DESIGN; DETECTION PERFORMANCE; FAULT DIAGNOSIS SCHEMES; FAULT DIAGNOSIS SYSTEMS; PHYSICAL MODEL; PROCESS DATA; PROCESS MODEL; RELATED VARIABLES; RESIDUAL GENERATOR;

MODELS; PROBABILITY DENSITY FUNCTION; PROCESS MONITORING;

FAULT DETECTION;

EID: 84870160474     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2012/832836     Document Type: Article

References (30)

1. Gertler J., Fault Detection and Diagnosis in Engineering Systems 1998 New York, NY, USA Marcel Dekker
2. Chen J., Patton R., Robust Model-Based Fault Diagnosis for Dynamic Systems 1999 Norwell, Mass, USA Kluwer Academic 10.1007/978-1-4615-5149-2 ZBL1235.70110
3. Patton R., Frank P., Clark R., Issues of Fault Diagnosis for Dynamic Systems 2000 Berlin, Germany Springer
4. Blanke M., Kinnaert M., Lunze J., Staroswiecki M., Schröder J., Diagnosis and Fault-Tolerant Control 2006 Berlin, Germany Springer ZBL1126.93004
5. Isermann R., Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance 2006 Berlin, Germany Springer
6. Ding S., Model-Based Fault Diagnosis Techniques 2008 Berlin, Germany Springer ZBL1243.68111
7. Ljung L., System Identification: Theory for the User 1987 Englewood Cliffs, NJ, USA Prentice Hall
8. Overschee P. V., Moor B. D., Subspace Identification for Linear Systems 1996 Dordrecht, The Netherlands Kluwer Academic
9. Favoreel W., De Moor B., Van Overschee P., Subspace state space system identification for industrial processes. Journal of Process Control 2000 10 2-3 149 155 2-s2.0-0033888152 10.1016/S0959-1524(99)00030-X (Pubitemid 30556712)
10. Qin S., An overview of subspace identification. Computers and Chemical Engineering 2006 30 1012 1502 1513 10.1016/j.compchemeng.2006.05.045 (Pubitemid 44292280)
11. Hwang I., Kim S., Kim Y., Seah C. E., A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology 2010 18 3 636 653 2-s2.0-77951880498 10.1109/TCST.2009.2026285
12. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S., A review of process fault detection and diagnosis. Part I: Quantitative model-based methods. Computers and Chemical Engineering 2003 27 293 311 (Pubitemid 36285962)
13. Zhang P., Ding S., On fault detection in linear discrete-time, periodic, and sampled-data systems (survey). Journal of Control Science and Engineering 2008 2008 19 849546
14. Shen B., Wang Z., Shu H., Wei G., Robust H ∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects. Automatica 2010 46 11 1743 1751 10.1016/j.automatica.2010.06.041 2878188
15. Shen B., Wang Z., Hung Y. S., Distributed H ∞ -consensus filtering in sensor networks with multiple missing measurements: the finite-horizon case. Automatica 2010 46 10 1682 1688 10.1016/j.automatica.2010.06.025 2877323
16. Dong H., Wang Z., Ho D. W. C., Gao H., Robust H ∞ filtering for Markovian jump systems with randomly occurring nonlinearities and sensor saturation: the finite-horizon case. IEEE Transactions on Signal Processing 2011 59 7 3048 3057 10.1109/TSP.2011.2135854 2839333
17. Dong H., Wang Z., Lam J., Gao H., Fuzzy-model-based robust fault detection with stochastic mixed time-delays and successive packet dropouts. IEEE Transactions on Systems, Man, and Cybernetics 2012 42 3, part B 365 376
18. Ding S. X., Zhang P., Huang B., Ding E. L., Subspace method aided data-driven design of observer based fault detection systems. Proceedings of the 16th Triennial World Congress of International Federation of Automatic Control (IFAC '05) July 2005 Prague, Czech Republic 167 172 2-s2.0-79960746611
19. Ding S., Yin S., Zhang P., Ding E., Naik A., An approach to data-driven adaptive residual generator design and implementation. Proceedings of the 7th IFAC Symposium on Fault Detection and Supervision and Safety of Technical Processes 2009 Barcelona, Spain
20. Ding S. X., Zhang P., Naik A., Ding E. L., Huang B., Subspace method aided data-driven design of fault detection and isolation systems. Journal of Process Control 2009 19 9 1496 1510 2-s2.0-72149132838 10.1016/j.jprocont.2009. 07.005
21. Yin S., Naik A., Ding S., Data-driven design of fault diagnosis scheme for periodic systems. Proceedings of the 7th Workshop on Advanced Control and Diagnosis 2009 Zielona Gora, Poland 10.1016/S0098-1354(02)00160-6
22. Ding S., Yin S., Wang Y., Wang Y., Yang Y., Ni B., Data-driven design of observers and its applications. Proceedings of the 18th IFAC World Congress 2011 Milano, Italy
23. Ding S., Zhang P., Ding E., Engel P., Gui W., A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis. Proceedings of the 18th IFAC World Congress 2011 Milano, Italy
24. Chiang L., Russell E., Braatz R., Fault Detection and Diagnosis in Industrial Systems 2001 London, UK Springer 10.1007/978-1-4471-0347-9 ZBL1209.62296
25. Ioannou P., Sun J., Robust Adaptive Control 1996 Prentice Hall
26. Tao G., Adaptive Control Design and Analysis 2003 Hoboken, NJ, USA Wiley-Interscience 10.1002/0471459100 1987484
27. Astrom K., Wittenmark B., Adaptive Control 1995 Addison-Wesley
28. Silverman B. W., Density Estimation for Statistics and Data Analysis 1986 London, UK Chapman & Hall 848134
29. Martin E., Morris A., Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control 1996 6 6 349 358 10.1016/0959-1524(96)00010-8 (Pubitemid 126375553)
30. Zhang X., Polycarpou M., Parisini T., Design and analysis of fault isolation scheme for a class of uncertain nonlinear systems. Annual Reviews in Control 2008 32 107 121 10.1016/j.arcontrol.2008.03.007