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Mathematical Problems in Engineering
Volume 2014, Article ID 692848, 9 pages
Research Article

Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR

1Laboratory of Intelligent Control and Robotics, Shanghai University of Engineering Science, Shanghai 201620, China
2College of Information Science and Technology, Donghua University, Shanghai 201620, China

Received 3 April 2014; Accepted 7 June 2014; Published 16 July 2014

Academic Editor: Jingjing Zhou

Copyright © 2014 Shengchao Su et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A robust online fault prediction method which combines sliding autoregressive moving average (ARMA) modeling with online least squares support vector regression (LS-SVR) compensation is presented for unknown nonlinear system. At first, we design an online LS-SVR algorithm for nonlinear time series prediction. Based on this, a combined time series prediction method is developed for nonlinear system prediction. The sliding ARMA model is used to approximate the nonlinear time series; meanwhile, the online LS-SVR is added to compensate for the nonlinear modeling error with external disturbance. As a result, the one-step-ahead prediction of the nonlinear time series is achieved and it can be extended to n-step-ahead prediction. The result of the n-step-ahead prediction is then used to judge the fault based on an abnormity estimation algorithm only using normal data of system. Accordingly, the online fault prediction is implemented with less amount of calculation. Finally, the proposed method is applied to fault prediction of model-unknown fighter F-16. The experimental results show that the method can predict the fault of nonlinear system not only accurately but also quickly.