A comparative study of just-in-time-learning based methods for online soft sensor modeling
A comparative study of just-in-time-learning based methods for online soft sensor modeling
复制标题
基于即时学习的在线软测量建模方法的比较研究
DOI：
10.1016/j.chemolab.2010.09.008
复制
发表时间：
2010-12-15
影响因子：
3.9
通讯作者：
Song, Zhihuan
中科院分区：
文献类型：
--
作者：
Ge, Zhiqiang;Song, Zhihuan
大多数传统的软传感器都是离线构建的，只能在线使用。在现代工业过程中，操作条件经常变化。对于这些时变过程，需要在线软传感器建模，因为预测结果与过程控制系统的其他组件高度相关。本文对基于偏最小二乘法（PLS）、支持向量回归（SVR）和最小二乘支持向量回归（LSSVR）的三种不同的在线软测量建模即时学习（JITL）方法进行了比较研究。与通过全局和离线方式对过程进行建模的传统软传感器不同，基于JITL的方法呈现出在线局部模型结构，依赖于该结构可以很好地跟踪过程的变化。此外，在此建模框架下还可以解决过程非线性问题。作为本文的进一步贡献，提出了一种实时性能改进策略来提高基于 JITL 的软传感器的在线建模效率。对于绩效评估，提供了两个工业案例研究。 (C) 2010 Elsevier B.V. 保留所有权利。
Most traditional soft sensors are built offline and only to be used online. In modern industrial processes, the operation condition is changed frequently. For these time-varying processes, online soft sensor modeling is required, since the prediction result is highly related to other components of the process control system. In the present paper, a comparative study of three different just-in-time-learning (JITL) methods for online soft sensor modeling is carried out, which are based on partial least squares (PLS), support vector regression (SVR) and least squares support vector regression (LSSVR). Different from traditional soft sensors which model the process through a global and offline manner, the JITL-based method exhibits an online local model structure, depending on which the change of the process can be well tracked. Besides, the process nonlinearity can also be addressed under this modeling framework. As a further contribution of this paper, a real-time performance improvement strategy is proposed to enhance the online modeling efficiency of the JITL-based soft sensor. For performance evaluation, two industrial case studies are provided. (C) 2010 Elsevier B.V. All rights reserved.