Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection
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- 作者：Bin Zhong ; Jing Wang ; Jinglin Zhou ; Haiyan Wu ; Qibing Jin
- 刊名：Industrial & Engineering Chemistry Research
- 出版年：2016
- 出版时间：February 17, 2016
- 年：2016
- 卷：55
- 期：6
- 页码：1609-1622
- 全文大小：1111K
- ISSN：1520-5045
文摘
A novel quality-related statistical process monitoring method based on global and local partial least-squares projection (QGLPLS) is proposed in this paper. The main idea of the QGLPLS method is to integrate the advantages of locality-preserving projections (LPP) and partial least squares (PLS) and extract meaningful low-dimensional representations of high-dimensional process and quality data. QGLPLS can exploit the underlying geometrical structure that contains both global and local information pertaining to the sampled data, including the process variable and quality variable measurements. It is well-known that the PLS method can find only the global structure information and ignores the local features of data sets and that the LPP method can preserve local features of data sets well without considering the product quality variables. The capacity for the preservation of global and local projections of the proposed method is compared to that of the PLS and LPP methods; the comparison results demonstrate that the QGLPLS method can effectively capture meaningful information hidden in the process and quality data. Next, a unified optimization framework, i.e., global covariance maximum and local graph minimum in the process measurement and quality data space, is constructed, and QGLPLS-based T2 and squared prediction error statistic control charts are developed for online process monitoring. Finally, two typical chemical processes, the Tennessee Eastman process and the penicillin fermentation process, are used to test the validity and effectiveness of the QGLPLS-based monitoring method. The experimental results show that the obtained process monitoring performances are better than those when using traditional monitoring methods, such as PLS, principal component analysis, LPP, and global–local structure analysis.