Transition Process Modeling and Monitoring Based on Dynamic Ensemble Clustering and Multiclass Support Vector Data Description

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• 作者：Zhibo Zhu ; Zhihuan Song ; Ahmet Palazoglu
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2011
• 出版时间：December 21, 2011
• 年：2011
• 卷：50
• 期：24
• 页码：13969-13983
• 全文大小：1413K
• 年卷期：v.50,no.24(December 21, 2011)
• ISSN：1520-5045

文摘

Monitoring and management of process transitions is a critical activity in chemical plants due to increased potential for abnormal operations. This activity is often hampered by the lack of a proper approach to label the transition states. In this paper, we present a systematic framework that constructs process transition states thus facilitating their monitoring for faulty operations. To address the nonstationary and non-Gaussian characteristics of the time series data collected during the transition process, an ensemble clustering method based on dynamic k-principal component analysis-independent component analysis (k-ICA-PCA) models is proposed to enable labeling of transitions. Next, we combine a PCA-based dimension reduction with a pattern classification strategy based on multiclass support vector data description (SVDD) to achieve transition process monitoring. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposed framework.