A Novel Statistical-Based Monitoring Approach for Complex Multivariate Processes

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• 作者：Zhiqiang Ge ; Lei Xie ; Zhihuan Song
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2009
• 出版时间：May 20, 2009
• 年：2009
• 卷：48
• 期：10
• 页码：4892-4898
• 全文大小：123K
• 年卷期：v.48,no.10(May 20, 2009)
• ISSN：1520-5045

文摘

Conventional methods are under the assumption that a process is driven by either non-Gaussian or Gaussian essential variables. However, many complex processes may be simultaneously driven by these two types of essential source. This paper proposes a novel independent component analysis and factor analysis (ICA-FA) method to capture the non-Gaussian and Gaussian essential variables. The non-Gaussian part is first extracted by ICA and support vector data description is utilized to obtain tight confidence limit. A probabilistic approach is subsequently incorporated to separate the residual Gaussian part into latent influential factors and unmodeled uncertainty. By retrieving the underlying process data generating structure, ICA-FA facilitates the diagnosis of process faults that occur in different sources. A further contribution of this paper is the definition of a new similarity factor based on the ICA-FA for fault identification. The efficiency of the proposed method is shown by a case study on the TE benchmark process.