Multiple sensor fault diagnosis by evolving data-driven approach

Information Sciences

Volumn 259, Issue , 2014, Pages 346-358

  El Koujok, M ^a   Benammar, M ^a   Meskin, N ^a   Al Naemi, M ^a   Langari, R ^b  

^a QATAR UNIVERSITY   (Qatar)

^b Texas AandM University   (United States)

Author keywords

Data driven approach; Nonlinear system; Sensor fault diagnosis

Indexed keywords

DATA-DRIVEN APPROACH; DESIGN AND DEVELOPMENT; MEASUREMENT DATA; MULTIPLE SENSORS; NONLINEAR PROCESS; PERFORMANCE CAPABILITY; SENSOR FAULT DIAGNOSIS; STIRRED TANK REACTORS;

ALGORITHMS; NONLINEAR SYSTEMS;

SENSORS;

EID: 84889689997     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.04.012     Document Type: Article

References (19)

1. J. Crossman, Y. Murphey, and J. Cardillo Automotive signal fault diagnostics, part I: signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection IEEE Transactions on Vehicular Technology 52 2003 63 75
2. T. Kobayashi, and D. Simon Hybrid neural-network genetic-algorithm technique for aircraft engine performance diagnostics Journal of Propulsion and Power 21 2005
3. H. Wang, T. Chai, J. Ding, and M. Brown Data driven fault diagnosis and fault tolerant control: Some advances and possible new directions Acta Automatica Sinica 35 2009 739 747
4. M. Napolitano, G. Silvestri, D.A.I. Windon, J. Casanova, and M. Innocenti Sensor validation using hardware-based on-line learning neural networks IEEE Transactions on Aerospace and Electronic Systems 34 2 1998 456 468
5. A. Jabbari, R. Jedermann, and W. Lang Application of computational intelligence for sensor fault detection and isolation World Academy of Science, Engineering and Technology 33 2007
6. Z. Zhang, J. Zhang, L. Ye, and Y. Zheng Research on the application of neural network in diaphragm icing sensor fault diagnosis 6th International Symposium on Neural Network, ISNN 2009 2009 Springer Wuhan, China
7. T. Takagi, and M. Sugeno Fuzzy identification of systems and its application to modelling and control IEEE Transactions on Systems, Man, and Cybernetics 15 1985 116 132
8. B. Castillo-Toledo, J. Anzurez-Marin, Model-based fault diagnosis using sliding mode observers to Takagi-Sugeno fuzzy model, in: Intelligent Control. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation.
9. W. Wang, M. Goldnaraghi, and F. Ismail Prognosis of machine health condition using neuro-fuzzy systems Mechanical Systems and Signal Processing 18 2004 813 831
10. R. Suja-Malar, and T. Thyagrajan Modelling of continuous stirred tank reactor using artificial intelligence techniques International Journal of Simulation and Modelling 8 2009 145 155
11. P. Tse, and D. Atherton Prediction of machine deterioration using vibration based fault trends and recurrent neural networks Journal of Vibration and Acoustics 121 1999 355 362
12. P. Angelov, and D. Filev An approach to online identification of Takagi-Sugeno fuzzy models IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 34 2004 484 498
13. P. Angelov, and N. Kasabov Evolving intelligent systems IEEE SMC eNewsLetter 2006 1 13
14. P. Angelov, and C. Xydeas Fuzzy systems design: direct and indirect approaches Soft Computing 10 2006 836 849
15. J.W. Hines, D.J. Wrest, and R.E. Uhrig Use of autoassociative neural networks for signal validation Journal of Intelligent and Robotic Systems 1998 143 154
16. M. Najafi, C. Gulp, R. Langari, Enhanced auto-associative neural networks for sensor diagnostics (e-aann), in: Fuzzy Systems. Proceedings: IEEE International Conference, 2005.
17. P. Frank, E.A. Garcia, and B. Kppen-Seliger Modelling for fault detection and isolation versus modelling for control Mathematics and Computers in Simulation 53 2000 259 271
18. I. Samy, I. Postlethwaite, and D.W. Gu Survey and application of sensor fault detection and isolation schemes Control Engineering Practice 19 2011 658 674
19. B.W. Bequette Process Dynamics: Modeling, Analysis and Simulation 1998 Prentice-Hall