.png){kind=icon}
返回
A lifecycle operating performance assessment framework for hot strip mill process based on robust kernel canonical variable analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conengprac.2020.104698.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the modern hot strip mill process (HSMP), the operating performance may deteriorate because of wear of equipment, mode transitions, and random disturbances. If the process is not adjusted and maintained, faults may occur, resulting in greater economic losses and potential safety hazards. Therefore, it is of great practical significance to carry out comprehensive operating performance assessment. In this paper, a lifecycle operating performance assessment framework based on robust kernel canonical variable analysis (RKCVA) is proposed to deal with automation hierarchy, nonlinearity, and outliers in the plant-wide HSMP. First, the HSMP is divided into upstream, midstream, and downstream in real-time control level (L1). Then, based on kernel canonical variable analysis (KCVA) and partial robust M-regression (PRM), the RKCVA models are developed for each stream and process control level (L2). Based on the Bayesian inference, statistical fusion is implemented to judge whether the process is in normal or faulty operating condition. After that, according to different evaluation rules of different operating conditions, the lifecycle operating performance assessment is realized. Finally, the framework is illustrated with a case study on a real HSMP. The assessment results show that the accuracy of the RKCVA is more than 10% higher than that of the KCVA.
Keyword:
Lifecycle operating performance assessment
Canonical variable analysis
Partial robust M-regression
Bayesian inference
Hot strip mill process
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
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms基于广义典型相关分析和随机算法的非高斯过程故障检测
PREAI
分享收藏分享收藏
查看更多内容