In this paper a novel process monitoring scheme is proposed because of the importance of fault detection and identification in industrial processes. In this method, process dynamic and effect of outliers are considered concurrently. First, the proposed approach uses CVA More
In this paper a novel process monitoring scheme is proposed because of the importance of fault detection and identification in industrial processes. In this method, process dynamic and effect of outliers are considered concurrently. First, the proposed approach uses CVA method to implement the process dynamic. Then ICA method is performed for dimension reduction of data. The outliers elimination and control limit calculation are based on the Local Outlier Factor algorithm. This algorithm doesn’t consider a special distribution for process variables, thus conforming to data in real industrial processes. The proposed method is applied to fault detection in the Tennessee Eastman process. Results clearly indicate better performance of the proposed scheme compared to the alternative methods.
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