A new method for fault prediction of model-unknown nonlinear system

Journal of the Franklin Institute

Volumn 345, Issue 2, 2008, Pages 136-153

  Zhengdao, Zhang ^a   Shousong, Hu ^b  

^a JIANGNAN UNIVERSITY   (China)

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

Density estimation; Fault prediction; Fighter; Neural network; Subspace identification; Tracking control; Unknown nonlinear system

Indexed keywords

CLOSED LOOP SYSTEMS; COMPUTER SIMULATION; FAULT DETECTION; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY DENSITY FUNCTION;

DENSITY ESTIMATION; FAULT PREDICTION; SUBSPACE IDENTIFICATION; TRACKING CONTROL;

NONLINEAR SYSTEMS;

EID: 38349178629     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2007.07.003     Document Type: Article

References (25)

1. Chang C.T., and Hwang J.I. Simplification techniques for EKF computations in fault diagnosis-suboptimal gains. Chem. Eng. Sci. 53 22 (1998) 3853-3862
2. Fathi Z., Ramirez W.F., and Korbiez. Analytical J. knowledge-based redundancy for fault diagnosis in process plants. AIChE J. 39 (1993) 42-56
3. Zhou D.H., and Frank P.M. Strong tracking filtering of nonlinear time-varying stochastic systems with colored noise with application to parameter estimation and empirical robustness analysis. Int. J. Control, 65 2 (1996) 295-370
4. Yang S.K., and Liu T.S. State estimation for predictive maintenance using Kalman filter. Reliab. Eng. Syst. Safety 66 1 (1999) 29-39
5. Ho S.L., and Xie M. The use of ARIMA models for reliability forecasting and analysis. Comput. Ind. Eng. 35 1,2 (1998) 213-216
6. Connor J.T., Martin D., and Atlas L.E. Recurrent neural networks and robust time series prediction. IEEE Trans. Neural Networks 5 2 (1994) 240-254
7. Tse P.W., and Atherton D.P. Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. J. Vibration Acoust. 121 7 (1999) 355-362
8. BuHamra S., Smaoui N., and Gabr M. The Box-Jenkins analysis and neural networks: prediction and time series modelling. Appl. Math. Model. 27 4 (2003) 805-815
9. Leung H., Lo T., and Wang S. Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Trans. Neural Networks 12 5 (2001) 1163-1172
10. Van Overschee P., and De Moor B. N4SID: subspace algorithms for the identification of combined deterministic and stochastic systems. Automatica 30 (1994) 75-93
11. Van Overschee P. Subspace Identification for Linear Systems-Theory, Implementation, Applications (1996), Kluwer, Boston, MA
12. Oku H., and Kimura H. Recursive 4SID algorithms using gradient type subspace tracking. Automatica 38 (2002) 1035-1043
13. Huang B., Ding S.X., and Joe Qin S. Closed-loop subspace identification; an orthogonal projection approach. J. Process Control 15 (2005) 53-66
14. Wang J., and Joe Qin S. A new subspace identification approach based on principal component analysis. J. Process Control 12 (2002) 841-855
15. Bodeux J.B., and Golinval J.C. Model identification and damage detection using the data-driven stochastic subspace and ARMAV methods [J]. Mech. Syst. Signal Process. 17 1 (2003) 83-89
16. Li W., and Raghavan H. Subspace identification of continuous time models for process fault detection and isolation [J]. J. Process Control 13 (2003) 407-421
17. C.T. Chou, M. Verhaegen, Identification of Wiener models with process noise, in: Proceedings of the 38th IEEE Conference on Decision and Control, Phoenix, Arizona, 1999.
18. Verhaegen M., and Westwick D. Identifying MIMO Hammerstein systems in the context of subspace model identification methods. Int. J. Control 63 2 (1996) 331-349
19. Favoreel W., De Moor B., and Van Overschee P. Subspace identification of bilinear systems subject to white inputs. IEEE Trans. Autom. Control 44 6 (1999) 1157-1165
20. H. Chen, J. M. Maciejowski. Subspace identification of combined deterministic-stochastic bilinear systems. in: IFAC Symposium on System Identification. Santa Barbara, California, 2000.
21. Seth L. Lacy, Dennis S. Bernstein, Subspace identification for nonlinear system that are linear in unmeasured states. in: Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida, USA, 2001, pp. 3518-3523.
22. V. Verdult, M. Verhaegen, J.M.A. Scherpen, Identification of nonlinear nonautonomous state space systems from input-output measurements, in: Proceedings of the IEEE International Conference on Industrial Technology, Goa, India, 2000, pp. 410-414.
23. Verdult V., and Verhaegen M. Subspace identification of multivariable linear parameter-varying systems. Automatica 38 (2002) 805-814
24. Park J., and Sandberg J.W. Universal approximation using radial basis functions network. Neural Comput. 3 (1991) 246-257
25. D.Y. Yeung, C.C. Chow, Parzen-Window network intrusion detectors, in: IEEE Proceedings of the 16th International Conference on Pattern Recognition, 2002, pp. 385-388.