In this study, a the two‐step support vector data description (TS‐SVDD) method is proposed to handle the problem of fault detection for dynamic, non‐linear, and non‐Gaussian processes. First, the dynamic structure of the data is identified and the data is divided into two components: innovation component and dynamic component. Then, the innovation component is used to make the SVDD model for fault detection. Moreover, in order to overcome the issue with two‐step principal component analysis (TS‐PCA) that the choice of parameters q and D affects the fault detection effect of the methods, a genetic algorithm (GA) is used to optimize the parameters. The proposed method combines the advantages of TS‐PCA in processing dynamic process data and SVDD in dealing with non‐linear and non‐Gaussian process data. In order to evaluate the effectiveness and superiority of the proposed method, TS‐SVDD is applied to the Tennessee Eastman (TE) process and the intelligent industrial processes control test facility (I²PC‐TF), and the fault detection performance is compared with TS‐PCA and SVDD in terms of dault detection rate (FDR) and false alarm rate (FAR). The results show that TS‐SVDD has a better monitoring performance.

中文翻译：

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用于动态，非线性和非高斯过程监控的两步支持向量数据描述

在这项研究中，提出了一种两步支持向量数据描述（TS-SVDD）方法来处理动态，非线性和非高斯过程的故障检测问题。首先，确定数据的动态结构，并将数据分为两个部分：创新部分和动态部分。然后，使用创新组件制作SVDD模型以进行故障检测。此外，为了克服两步主成分分析（TS-PCA）的问题，选择参数q和D影响方法的故障检测效果，使用遗传算法（GA）优化参数。所提出的方法结合了TS-PCA在处理动态过程数据方面的优势和SVDD在处理非线性和非高斯过程数据方面的优势。为了评估该方法的有效性和优越性，将TS‐SVDD用于田纳西州伊士曼（TE）过程和智能工业过程控制测试设备（I ² PC‐TF），并将故障检测性能与TS-PCA和SVDD在故障检测率（FDR）和误报率（FAR）方面。结果表明，TS‐SVDD具有更好的监视性能。