Fault prediction for nonlinear time series based on neural network

Zidonghua Xuebao/Acta Automatica Sinica

Volumn 33, Issue 7, 2007, Pages 744-748

  Hu, Shou Song ^a   Zhang, Zheng Dao ^b  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b JIANGNAN UNIVERSITY   (China)

Author keywords

Density estimation; Fault prediction; Moving horizon prediction; Neural network; Nonlinear time series

Indexed keywords

DYNAMICAL SYSTEMS; ESTIMATION; OPTIMIZATION; PROBABILITY DENSITY FUNCTION;

DENSITY ESTIMATION; FAULT PREDICTION; MOVING HORIZON PREDICTION; NONLINEAR TIME SERIES;

NEURAL NETWORKS;

EID: 34547981016     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.1360/aas-007-0744     Document Type: Article

References (11)

1. Ho S L, Xie M. The use of ARIMA models for reliability forecasting and analysis. Computers and Industrial Engineering, 1998, 35(1): 213-216.
2. Zhang Z D, Hu S S. Modeling time series based fault prediction for model-unknown nonlinear system. In: Proceedings of the 2nd International Conference on Impulsive Dynamical System and Applications. Wuxi, China, 2002. 1641-1649.
3. Wedding D K, Cios K J. Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neurocomputing, 1996, 10(2): 149-168.
4. Zhang G P, Patuwo B E, Hu M Y. A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers and Operations Research, 2001, 28(3): 381-396.
5. Cowper M R, Mulgrew B, Unsworth C P. Nonlinear prediction of chaotic signals using a normalized radial basis function network. Signal Processing, 2002, 82(5): 775-789.
6. Tse P W, Atherton D P. Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. Journal of Vibration and Acoustics, 1999, 121(7): 355-362.
7. Xu K, Xie M, Tang L C, Ho S L. Application of neural networks in forecasting engine systems reliability. Applied Soft Computing, 2003, 2(4): 255-268.
8. BuHamra S, Smaoui N. The Box-Jenkins analysis and neural networks: prediction and time series modeling. Applied Mathematical Modeling, 2003, 27(10): 805-815.
9. Henry L, Titus L, Wang S C. Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Transactions on Neural Networks, 2001, 12(5): 1163-1172.
10. Park J, Sandberg J W. Universal approximation using radial basis functions network. Neural Computation, 1991, 3(2): 246-257.
11. Yeung D Y, Chow C. Parzen-window network intrusion detectors. In: Proceedings of the 16th International Conference on Pattern Recognition. IEEE, 2002. 4: 385-388.