Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models
Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models
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DOI：
10.1016/j.compchemeng.2013.06.014
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发表时间：
2013-11-11
影响因子：
4.3
通讯作者：
Kadlec, Petr
中科院分区：
文献类型：
--
作者：
Grbic, Ratko;Sliskovic, Drazen;Kadlec, Petr
在处理非线性和多模态过程时，线性模型可能不适用，这会导致软测量器性能不佳。由于过程行为随时间变化，有必要推导并实施某种自适应机制，以将软测量器性能保持在期望水平。因此，本文提出了一种基于混合高斯过程回归模型的软测量器自适应机制。还介绍了一种基于互信息的输入变量选择方法。该方法为输出变量预测选择最重要的输入变量，从而简化了模型的开发和自适应过程。除了对难以测量的变量进行在线预测外，这种软测量器还可用于自适应过程监测。通过田纳西 - 伊斯曼过程以及两个实际工业案例，将所提方法的效率与常用的递推偏最小二乘法和递推主成分分析法进行了对比。(C) 2013爱思唯尔有限公司。保留所有权利。
Linear models can be inappropriate when dealing with nonlinear and multimode processes, leading to a soft sensor with poor performance. Due to time-varying process behaviour it is necessary to derive and implement some kind of adaptation mechanism in order to keep the soft sensor performance at a desired level. Therefore, an adaptation mechanism for a soft sensor based on a mixture of Gaussian process regression models is proposed in this paper. A procedure for input variable selection based on mutual information is also presented. This procedure selects the most important input variables for output variable prediction, thus simplifying model development and adaptation. Apart from online prediction of the difficult-to-measure variable, this soft sensor can be used for adaptive process monitoring. The efficiency of the proposed method is benchmarked with the commonly applied recursive PLS and recursive PCA method on the Tennessee Eastman process and two real industrial examples. (C) 2013 Elsevier Ltd. All rights reserved.