.png){kind=icon}
返回
Multiple model based LPV soft sensor development with irregular/missing process output measurement
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conengprac.2011.10.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven soft sensors have been applied extensively in process industry for process monitoring and control. Linear soft sensors, which are only valid within a relatively small operating envelope, are considered to be insufficient in practice when the processes transit among several operating modes. Moreover, owing to a variety of causes such as malfunction of sensors, multiple rate sampling scheme for different process variables, etc., missing data problem is commonly experienced in process industry. In this paper, soft sensor development with irregular/missing output data is considered and a multiple model based linear parameter varying (LPV) modeling scheme is proposed for handling nonlinearity. The efficiency of the proposed algorithm is demonstrated through several numerical simulation examples as well as a pilot-scale experiment. It is shown through the comparison with the traditional missing data treatment methods in terms of the parameter estimation accuracy that the developed soft sensors enjoy improved performance by employing the expectation-maximization (EM) algorithm in handling the missing process data and model switching problem. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Bayesian methods
Soft sensors
Expectation-maximization method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.6
论文数:
5.7K
被引数:
1.1W
机构
引用论文
Optimal Design of DER for Economic/Environmental Sustainability of Local Energy Communities当地能源社区经济/环境可持续性的DER优化设计
.png){kind=icon}
PREAI
分享收藏Inferential control system of distillation compositions using dynamic partial least squares regression使用动态偏最小二乘回归的蒸馏成分推理控制系统
PREAI
分享收藏
分享收藏分享收藏
查看更多内容