一种基于多工况识别的过程在线监测方法
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作者单位:

(1. 江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122;2. 江南大学物联网工程学院, 江苏无锡214122)

作者简介:

熊伟丽($1978-$), 女, 教授, 博士, 从事复杂工业过程建模及优化、智能优化算法及应用等研究;郭校根(1991-), 男, 硕士生, 从事工业过程监控的研究.

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E-mail: greenpre@163.com

中图分类号:

TP273

基金项目:

国家自然科学基金项目(61773182,21206053);江苏省“六大人才高峰”计划项目(2013-DZXX-043).


A process on-line monitoring method based on multi-mode identification
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Affiliation:

(1. China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education,Jiangnan University,Wuxi 214122,China;2. School of the Internet of Things Engineering,Jiangnan University,Wuxi 214122,China)

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    摘要:

    在包含多个工况的工业生产过程中,各个稳态工况之间存在着一定的过渡过程,虽然过渡时间较短,但其复杂的动态特性使得传统的过程监测方法难以获得满意的效果,为此提出一种基于多工况识别的过程监测方法.首先,通过窗口切割对基本稳态工况进行识别;然后,采用滑动窗技术确定过渡过程的起始和结束时间,并进一步基于差分分段技术对过渡过程的子阶段进行分类,考虑到各阶段数据的不同分布特性,利用独立成分分析和主元分析分别提取各阶段数据的非高斯和高斯信息;最后根据贝叶斯推断将3个统计量进行重构, 实现多工况过程的在线监测.通过TE过程的仿真研究,验证了所提出方法的可行性和有效性.

    Abstract:

    In the industrial production process containing a number of different modes, there is a certain transition process between every two steady modes. Although the transition period is rather short, its the complex dynamic behavior makes traditional process monitoring methods difficult to obtain satisfactory results. Therefore, a process monitoring method based on multi-mode identification is proposed. Firstly, the identification of the basic steady modes is realized through a window cutting method. Then, the accurate time boundaries of transitional modes are determined by using a moving window strategy, and a differential segmentation technique is performed on the sub-modes of the transitional process for classification. Considering different distribution characteristics of each data segment, independent component analysis and principal component analysis are carried out to deal with the non-Gaussian and Gaussian information. Finally, according to the Bayesian inference, three statistics are reconstructed to realize the multi-mode process monitoring. The feasibility and effectiveness of the proposed method are later demonstrated through a simulated Tennessee Eastman(TE) process.

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熊伟丽,郭校根.一种基于多工况识别的过程在线监测方法[J].控制与决策,2018,33(3):403-412

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  • 在线发布日期: 2018-03-06
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