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中文摘要：
针对动态自相关数据的特征提取和降维问题,提出了一种基于时序扩展的邻域保持嵌入（TNPE）的故障检测方法。针对邻域保持嵌入算法的不足,构建了新的优化目标,在构建局部空间结构特征的基础上,同时提取了数据随时间变化的动态特征。使得投影得到的低维空间不仅和原始变量空间具有相似的空间局部近邻结构,而且具有相似的时序动态结构,因而包含了更多的特征信息。在此基础上,利用TNPE算法将原始过程数据划分为特征空间和残差空间,并分别建立T2和SPE统计量实现工业过程监测。通过对Tennessee Eastman（TE）过程的仿真研究,验证了TNPE算法有效性可行性,并显示出了优越的故障检测能力。
英文摘要：
In order to handle the feature extraction and dimension reduction of dynamic autocorrelation data,this paper presents a temporal extension method of neighborhood preserving embedding（TNPE）for fault detection.Taking the limitation of the existing NPE into account,a new optimizing target is constructed by incorporating both the spatial feature and the temporal relation among the process data. Comparing to original high-dimensional variable space,the obtained low-dimensional projection space has similar local spatial structure and the temporal dynamic structure,such that more feature information can be extracted.By means of the TNPE algorithm,the original variable space is divided into the feature space and residual space.Moreover,Hotelling＇s T2 and squared predication error are constructed upon the TNPE model to monitor the variations among the two spaces.A case study on the Tennessee Eastman process demonstrates the feasibility and efficacy of the proposed method in this paper,which also shows the superiority in fault detection.
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