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Process data compression and trending can improve factory control system performances. Swing door trending (SDT) algorithm is well designed to adapt the process trend while retaining the merit of simplicity. But it cannot handle outliers and adapt to fluctuations of actual data. Methodological improvements on the existing SDT algorithm are proposed in this paper. The effectiveness and applicability of improved SDT (ISDT) algorithm are demonstrated by computations on both synthetic and real process data. By applying an adaptive recording limit together with outliers-detecting rules, a higher compression ratio is achieved and outliers are identified and eliminated. The fidelity is only influenced slightly. It can be used both in online and batch mode, and integrated into existing software packages without any change.
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on (Volume:3 )
Date of Conference: 2002