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首页> 外文期刊>Journal of control, automation and electrical systems >Novel Nonlinear Process Monitoring and Fault Diagnosis Method Based on KPCA-ICA and MSVMs
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Novel Nonlinear Process Monitoring and Fault Diagnosis Method Based on KPCA-ICA and MSVMs

机译：基于KPCA-ICA和MSVM的非线性过程监测与故障诊断新方法

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摘要

A novel nonlinear process monitoring method based on kernel principal component analysis (KPCA)-independent component analysis (ICA) and multiple support vector machines (MSVMs) is proposed. KPCA pretreats data and makes the data structure become as linearly separable as possible. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-Gaussian as possible. MSVMs is applied for identification of different fault sources. The application to Tennessee Eastman process indicates that the proposed method can effectively capture the nonlinear relationship in process variables and has good diagnosis capability and overall diagnosis correctness rate.
机译：提出了一种基于核主成分分析（ICA）和多支持向量机（MSVM）的非线性过程监测方法。 KPCA对数据​​进行预处理，并使数据结构尽可能线性可分离。 ICA在KPCA增白空间中寻找投影方向，从而使投影数据的分布尽可能地非高斯。 MSVM用于识别不同的故障源。在田纳西州伊士曼过程中的应用表明，该方法可以有效地捕获过程变量中的非线性关系，具有良好的诊断能力和总体诊断正确率。

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