Time Neighborhood Preserving Embedding Model and Its Application for Fault Detection
Time Neighborhood Preserving Embedding Model and Its Application for Fault Detection
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DOI：
10.1021/ie400854f
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发表时间：
2013-09
影响因子：
4.2
通讯作者：
Aimin Miao;Zhiqiang Ge;Zhihuan Song;Le Zhou
中科院分区：
文献类型：
--
作者：
Aimin Miao;Zhiqiang Ge;Zhihuan Song;Le Zhou
结合过程数据的序列相关性，提出了一种新的降维方法--时间邻域保持嵌入法（TNPE），并将其应用于动态过程故障检测。TNPE的目的是保持数据的局部邻域结构，以学习底层的几何流形。为了在构造邻域图时描述动态特性，针对每个数据点的时间序列相邻点执行最近邻搜索。此外，局部动态变化的高维数据被捕获重建每个数据点从其最近的邻居。基于所选邻域的局部线性性质，估计的局部结构信息对线性投影变换具有鲁棒性。因此，通过考虑序列相关性，TNPE能够探索隐藏在高维数据中的有意义的动态信息。对于故障检测，Hotelling的T2...
By incorporating the serial correlations of the process data, a new dimensionality reduction method, named time neighborhood preserving embedding (TNPE) is proposed and applied for dynamic process fault detection. TNPE aims at preserving the local neighborhood structure of the data to learn the underlying geometry manifold. To describe the dynamic characteristic while constructing the neighborhood graph, the search for nearest neighbors is performed with respect to the time sequence adjacent points of each data point. Furthermore, the local dynamic variations of the high-dimensional data are captured by reconstructing each data point from its nearest neighbors. On the basis of the locally linear property of the selected neighbors, the estimated local structure information is robust to the transformations of linear projection. Consequently, by considering the serial correlations, TNPE is able to explore the meaningful dynamic information hidden in high-dimensional data. For fault detection, Hotelling’s T2 ...