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Improved PCA-SVDD based monitoring method for nonlinear process

机译：基于PCA-SVDD的非线性过程监测方法

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摘要

Conventional principal component analysis (PCA) is limited to Gaussian process data due to its monitoring statistics. This paper introduces an improved PCA based method for nonlinear process monitoring using support vector data description (SVDD) by constructing two new monitoring statistics. Different from the traditional PCA method, monitoring statistics based on SVDD model have no Gaussian assumption. Thus the new monitoring statistics have no restriction to the distribution of process data, which is effective for nonlinear process monitoring. A corresponding fault diagnosis method is also proposed. To demonstrate the efficiency, detailed comparisons between the new approach and conventional methods are presented. The monitoring performance of the proposed method is examined through a numerical example and the Tennessee Eastman (TE) benchmark process.
机译：传统的主要成分分析（PCA）由于其监测统计数据而限于高斯过程数据。本文介绍了一种改进的基于PCA的非线性过程监控方法，通过构建两个新的监视统计来使用支持向量数据描述（SVDD）。与传统的PCA方法不同，基于SVDD模型的监测统计数据没有高斯假设。因此，新的监测统计数据对过程数据的分布没有限制，这对于非线性过程监测有效。还提出了相应的故障诊断方法。为了证明效率，提出了新方法与传统方法的详细比较。通过数值示例和田纳西州伊斯特曼（TE）基准过程来检查所提出的方法的监测性能。

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