| 引用本文: | 李文卿,赵春晖,孙优贤.不等长批次过程的有序时段划分、建模及故障检测[J].控制理论与应用,2015,32(9):1226~1232.[点击复制] |
| LI Wen-qing,ZHAO Chun-hui,SUN You-xian.Sequential unequal-length phase identification and modelling method for fault detection of varying-duration batch processes[J].Control Theory and Technology,2015,32(9):1226~1232.[点击复制] |
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| 不等长批次过程的有序时段划分、建模及故障检测 |
| Sequential unequal-length phase identification and modelling method for fault detection of varying-duration batch processes |
| 摘要点击 2569 全文点击 1319 投稿时间：2015-03-26 修订日期：2015-07-15 |
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| DOI编号 10.7641/CTA.2015.50238 |
| 2015,32(9):1226-1232 |
| 中文关键词 批次过程 不等长批次 不等长时段识别 多元统计建模 故障检测 |
| 英文关键词 batch processes varying batch durations unequal-length phase identification multivariate statistical mod- eling fault detection |
| 基金项目 国家自然科学基金;省自然科学基金;新世纪优秀人才计划基金；教育部留学回国人员科研基金； |
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| 中文摘要 |
| 对具有不等长时段的多时段批次过程进行监测是十分重要而且具有一定难度的. 时段在批次间的错位现象导致时间方向的不同过程特性混合在一起, 这给时段分析以及在线应用带来了一系列的问题. 为了解决不等长所带来的问题, 本文提出一种基于不等长时段有序识别及建模的故障检测方法. 该方法的主要贡献包括以下方面：1) 该方法通过步进地衡量过程的变量相关性对模型精度以及监测性能的影响, 自动有序地识别出每个不等长时段; 2) 在每个时段内,通过对不规则的过程数据进行整合建立了时段模型以捕捉不规则的时段特性; 3) 本文提供了一种简单而有效的在线判断新样本隶属时段和监测其运行状态的方法. 最后, 本文通过一个实例–具有不等长批次长度的注塑过程阐述了本方法的有效性. |
| 英文摘要 |
| The work of monitoring multiphase batch processes with unequal phase durations is of great importance
but difficult. Due to misaligned phases, process characteristics are mixed along time direction which causes problems in
phase analysis and modeling as well as online application. In order to solve the uneven-length problem, this paper proposes
a sequential unequal-length phase identification and modeling-based fault detection method. The main contribution of
the proposed method includes: 1) multiple unequal-length phases are sequentially identified by evaluating the changes
of process variable correlations step-wise regarding their influences on model accuracy and monitoring performance; 2)
irregular phase characteristics are captured by irregular data-arranging-based modeling strategy; 3) the proposed method
provides an easy but effective way to judge the phase affiliation and check the operation statuses of new samples in real
time. Its online monitoring performance is illustrated by an injection molding process with varying durations. |