Online fault prediction for nonlinear system based on sliding ARMA combined with online LS-SVR

In this paper, 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. A combined time series prediction method is then 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. The one-step-ahead prediction of the nonlinear time series is so achieved. Finally, the online method is applied into motor time series polluted by noise, and a fault decision function is defined to judge the fault information manifested by the predicted error. The experimental results show the effectiveness of the proposed method.