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首页> 外文期刊>AIChE Journal >A Support Vector Clustering-Based Probabilistic Method for Unsupervised Fault Detection and Classification of Complex Chemical Processes Using Unlabeled Data
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A Support Vector Clustering-Based Probabilistic Method for Unsupervised Fault Detection and Classification of Complex Chemical Processes Using Unlabeled Data

机译：基于支持向量聚类的概率性方法，用于使用未标记数据的复杂化学过程的无监督故障检测和分类

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摘要

A new support vector clustering (SVC)-based probabilistic approach is developed for unsupervised chemical process monitoring and fault classification in this article. The spherical centers and radii of different clusters corresponding to normal and various kinds of faulty operations are estimated in the kernel feature space. Then the geometric distance of the monitored samples to different cluster centers and boundary support vectors are computed so that the distance-ratio-based probabilistic-like index can be further defined. Thus, the most probable clusters can be assigned to the monitored samples for fault detection and classification. The proposed SVC monitoring approach is applied to two test scenarios in the Tennessee Eastman Chemical process and its results are compared to those of the conventional K-nearest neighbor Fisher discriminant analysis (KNN-FDA) and K-nearest neighbor support vector machine (KNN-SVM) methods. The result comparison demonstrates the superiority of the SVC-based probabilistic approach over the traditional KNN-FDA and KNN-SVM methods in terms of fault detection and classification accuracies.
机译：本文针对无监督的化学过程监控和故障分类，开发了一种基于新的基于支持向量聚类（SVC）的概率方法。在核特征空间中，估计了对应于正常和各种故障操作的不同簇的球心和半径。然后，计算受监视样本到不同聚类中心和边界支持向量的几何距离，以便可以进一步定义基于距离比的概率似指数。因此，可以将最可能的群集分配给监视的样本，以进行故障检测和分类。拟议的SVC监测方法应用于田纳西伊士曼化学工艺的两个测试场景，并将其结果与传统的K近邻Fisher判别分析（KNN-FDA）和K近邻支持向量机（KNN- SVM）方法。结果比较表明，在故障检测和分类准确性方面，基于SVC的概率方法优于传统的KNN-FDA和KNN-SVM方法。

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