Robust modeling of mixture probabilistic principal component analysis and process monitoring application
Robust modeling of mixture probabilistic principal component analysis and process monitoring application
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DOI：
10.1002/aic.14419
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发表时间：
2014-06
期刊：
影响因子：
3.7
通讯作者：
Jinlin Zhu;Zhiqiang Ge;Zhihuan Song
中科院分区：
文献类型：
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作者：
Jinlin Zhu;Zhiqiang Ge;Zhihuan Song
提出了一种混合概率主成分分析（PPCA）鲁棒建模策略。与传统的高斯分布驱动模型如PPCA不同，采用多元学生t分布进行概率建模，以减少过程工业中常见的异常值的负面影响。此外，为了处理丢失数据问题，部分更新算法的鲁棒混合PPCA模型的参数学习。因此，新的鲁棒模型可以同时处理异常值和缺失数据。对于过程监控，提出了一种贝叶斯软决策融合策略，该策略结合了不同工况下的鲁棒局部监控模型。两个案例研究表明，新的鲁棒模型显示出增强的建模和监测性能在离群值和缺失数据的情况下，相比的混合概率主分析模型。© 2014美国化学工程师学会AIChE J，60：2143-2157，2014
In this article, a robust modeling strategy for mixture probabilistic principal component analysis (PPCA) is proposed. Different from the traditional Gaussian distribution driven model such as PPCA, the multivariate student t-distribution is adopted for probabilistic modeling to reduce the negative effect of outliers, which is very common in the process industry. Furthermore, for handling the missing data problem, a partially updating algorithm is developed for parameter learning in the robust mixture PPCA model. Therefore, the new robust model can simultaneously deal with outliers and missing data. For process monitoring, a Bayesian soft decision fusion strategy is developed which is combined with the robust local monitoring models under different operating conditions. Two case studies demonstrate that the new robust model shows enhanced modeling and monitoring performance in both outlier and missing data cases, compared to the mixture probabilistic principal analysis model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2143–2157, 2014