Sparse Representation Residual Space Analysis and Its Application to Multimode Batch Process Monitoring

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• 作者：Zhibo Xiao ; Huangang Wang
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2016
• 出版时间：January 13, 2016
• 年：2016
• 卷：55
• 期：1
• 页码：187-196
• 全文大小：666K
• ISSN：1520-5045

文摘

In this paper, a novel multimode batch process monitoring scheme called sparse representation residual space analysis (SR-RA) is proposed. The proposed SR-RA method first unfolds the three way batch data into a two way matrix, where each batch is represented as a vector. By utilizing robust sparse representation (SR), the proposed method decomposes the original high dimensional multimode data into three subspaces: the SR primal subspace which preserves all the mode relevant information, the Gaussian residual subspace which is single modal Gaussian distributed, and the outlier residual subspace which is sparsely distributed with only a few nonzero entries. As the fault information is all effectively captured in the two residual subspaces which are mode-irrelevant, fault detection is performed in the two residual subspaces. The proposed method can effectively deal with the multimode problem and it inherits the robustness of SR to large magnitude outliers. The superiority of the proposed SR-RA method is verified on a benchmark semiconductor etching problem.