一种复杂机电系统LE-SVDD异常监测方法
Laplacian Eigenmaps Support Vector Domain Description Method for Complex Electromechanical System

复杂机电系统生产过程监测数据具有明显的高维非线性和复杂分布特点，针对传统的方法难以满足复杂系统异常辨识的要求，提出一种拉普拉斯特征映射-支持向量数据描述（Laplacian eigenmaps-support vector domain description，简称LE-SVDD）的异常监测方法。由于高维特征空间中距离很近的点投影到低维空间后距离应该很近，因此改进的LE方法使用一个有权向图来描述一个流行，用嵌入的方式找到高维数据的低维嵌入，从而能够发现高维数据内部的地位流行结构。通过标准的田纳西 伊斯曼过程（Tennessee Eastman process，简称TE过程）测试和训练数据进行仿真实验，给出了在非线性特征提取和不同时段异常辨识的准确结果。平均漏报率和误报率都比较低，分别为6.063，6和5.625，3.125，这表明LE-SVDD方法在状态监测中具有良好的非线性和高维数据处理能力，适用于工程系统的监测诊断。
The monitoring data of the complex electromechanical system has obvious high-dimensional nonlinear and complex distribution characteristics. In order to meet the requirements of complex system anomaly identification which are difficult to be satisfied by the traditional method, a kind of Laplacian eigenmaps-support vector domain description method (LE-SVDD) is proposed. On account of the fact that the points which are close in the high-dimensional feature space should also be close after being projected to the low-dimensional feature space, the improved LE method uses a weighted undirected graph to describe a popularity and find the low-dimensional embedment with an embedded method, thereby status popular structures can be found in the high-dimensional data. In the simulation experiments based on the standard Tennessee-Eastman process (TE process) test and training data, the accurate results of nonlinear feature extraction and anomaly identification at different time are given. Respectively, the average false negative rate and false alarm rate are 6.063, 6 and 5.625, 3.125, which are relatively low. It shows that LE-SVDD method has excellent non-linearity and high-dimensional data processing capability in condition monitoring, which is suitable for the monitoring and diagnosis of engineering system.