Application of multi-step time series prediction for industrial equipment prognostic

The use of prognostics is critically to be implemented in industrial. This paper presents an application of multi-step time series prediction to support industrial equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which is able to predict the series of future equipment condition. The structure of prognostic application is presented. The feasibility of this prediction application was demonstrated by applying real condition monitoring data of industrial equipment.