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首页> 外文期刊>International journal of system control and information processing >Enhanced process monitoring via time-space coordinated-locality preserving projection
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Enhanced process monitoring via time-space coordinated-locality preserving projection

机译：通过时间空间协调局部保留投影增强过程监控

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摘要

In this paper, a novel dimensionality reduction method, time-space coordinated-locality preserving projections (TSC-LPP) is proposed based on locality preserving projection (LPP). In practical process, except the data correlation in spatial scale, there exists data correlation in time scale as well for the short sampling interval. To considering the correlation of sampling points in time and spatial scale simultaneously, TSC-LPP constructs the adjacency graph by selecting adjacent points in time sequence and Euclidean distance, respectively. Furthermore, the importance of the time-sequential neighbours is measured by the computed weight based on time distance. A dual objective function with a weight index coordinating the relationship between time and space is constructed to compute the transformation matrix. Hotelling's T~2 and squared prediction error (SPE) are established for process monitoring. A numerical case and the Tennessee-Eastman process (TEP) are employed for the experimental verification.
机译：本文基于位置保存投影（LPP）提出了一种新的二维分裂减少方法，时间空间协调局部保存投影（TSC-LPP）。在实际过程中，除了空间尺度中的数据相关性，对于短的采样间隔，还存在时间尺度的数据相关性。为了同时考虑采样点和空间刻度的采样点的相关性，TSC-LPP分别通过在时间序列和欧几里德距离中选择相邻点来构造邻接图。此外，基于时间距离通过计算权重测量时间顺序邻居的重要性。构建具有权重指数的双目标函数，用于协调时间和空间之间的关系以计算变换矩阵。为过程监控建立了Hotelling的T〜2和平方预测误差（SPE）。实验验证，采用了数值案例和田纳西州 - 伊斯曼进程（TEP）。

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