中文摘要：

为解决实际工业过程中的非线性和非高斯问题，实现有效的过程监控，提出了一种基于局部切空间排列算法的过程监控方法。首先运用局部切空间排列算法对标准化后的正常样本数据提取出低维子流形以实现维数约减。之后利用Greedy方法提取特征样本以支持向量数据描述方法建立监控模型，最后采用相应统计量进行过程监控。以田纳西伊斯曼（TE）模型为仿真平台，仿真结果说明了该方法的有效性。

英文摘要：

There are some characteristics such as non-linearity and non-Gaussian in real industrial process data. Aiming at these problems, a process monitoring method based on the local tangent space alignment is proposed. Firstly, the local tangent space alignment algorithm is used to get the sub-manifold of low dimension from the normalized normal sample data such that the dimension reduction can be achieved. Then, Greedy method is used to extract feature sample to establish the monitoring model by support vector data description. Finally, the corresponding statistic is used for process monitoring. The simulation is made on the TE model, whose results illustrate the effectiveness of the proposed method.