基于CVDA与LLE算法的工业过程故障检测  

Industrial process fault detection based on CVDA and LLE algorithms

作　　者：蒋磊峰 张成[2] 李元 Jiang Leifeng;Zhang Cheng;Li Yuan(College of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110142,China;College of Science,Shenyang University of Chemical Technology,Shenyang 110142,China)

机构地区：[1]沈阳化工大学信息工程学院,沈阳110142 [2]沈阳化工大学理学院,沈阳110142

出　　处：《电子测量技术》2024年第15期169-176,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(62273242)项目资助。

摘　　要：针对工业过程数据中存在的非线性、高维度的问题,提出一种结合典型变量差异度分析与局部线性嵌入算法的故障检测方法。CVDA算法构建的差异度矩阵能实现有效的故障监测,但其依赖于线性投影,仅对数据结构中线性特征的变化敏感。使用LLE算法通过保持样本间的局部关系,将高维的数据映射到低维空间,进行特征的再次提取,进一步挖掘数据的非线性特征和局部邻域信息。最后在低维流形空间中建立隔离森林模型,将得到样本点的异常分数作为故障检测评价标准。通过一组非线性数值实例和TE化工过程数据,将本文所提方法与传统的KPCA、PPA以及CVDA进行对比分析,验证所提算法的有效性及优越性。To address the issues of nonlinearity and high dimensionality in industrial process data,a fault detection method combining Canonical Variate Dissimilarity Analysis and Locally Linear Embedding is proposed.The dissimilarity matrix constructed by the CVDA algorithm can effectively monitor faults,but it relies on linear projections and is only sensitive to changes in linear features of the data structure.The LLE algorithm is used to map high-dimensional data to a low-dimensional space by preserving local relationships between samples,further extracting features and uncovering nonlinear characteristics and local neighborhood information.Finally,an isolation forest model is established in the low-dimensional manifold space to obtain anomaly scores of sample points as the fault detection evaluation criterion.Through a set of nonlinear numerical examples and the Tennessee Eastman chemical process data,the proposed method is compared and analyzed with traditional KPCA、PPA and CVDA to verify its effectiveness and superiority.

关 键 词：典型变量差异度分析 非线性过程 局部线性嵌入 隔离森林 

分 类 号：TN081[电子电信—物理电子学]