【摘 要】
：
Operating at different operation steps,process variable correlations of batch processes present the typical multiphase and transition characteristics.The existing phase partition algorithms have sever
【机 构】
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State Key Laboratory of Industrial Control Technology,Department of Control Science and Engineering,
论文部分内容阅读
Operating at different operation steps,process variable correlations of batch processes present the typical multiphase and transition characteristics.The existing phase partition algorithms have several disadvantages,such as time sequence disorder,missing monitoring performance,unavailable quantitative index indicating transition patterns,and dependence on process prior knowledge.In particular,identification of transition patterns do not have the related theory support.To effectively overcome these problems,in this work,a self-optimization sequential phase partition and transition identification(SSPTI)algorithm is proposed.Combining process variable correlations,influence degree on monitoring performance are self-optimized for phase partition without key parameters and prior process knowledge.Moreover,a quantitative index based on density and distance from the perspective of clustering analysis is further defined to indicate transition patterns.
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