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Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

ISSN号：1004-9541
期刊名称：《中国化学工程学报：英文版》
时间：0
分类：TP277[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置] O212.4[理学—概率论与数理统计;理学—数学]
作者机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, EastChina University of Science and Technology, Shanghai 200237, China
相关基金：Supported by the National Basic Research Program of China （2013CB733600）, the National Natural Science Foundation of China （21176073）, the Doctoral Fund of Ministry of Education of China （20090074110005）, the Program for New Century Excellent Talents in University （NCET-09-0346）, Shu Guang Project （09SG29） and the Fundamental Research Funds for the Central Universities.

关键词： FISHER判别分析, 故障诊断方法, 自组织映射, 化工过程, 集成, 化学过程, 异常状态, 实时监测, self-organizing maps, Fisher discriminant analysis, fault diagnosis, monitoring, Tennessee Eastman process

中文摘要：

差错诊断并且监视为复杂化学过程是很重要的。有众多的方法，在这个领域，有效可视化方法仍然在是挑战性的里被学习了。以便得到更好的可视化效果，把自我组织的地图(SOM ) 与菲希尔判别式分析(食物及药品管理局) 相结合的一个新奇差错诊断方法被建议。食物及药品管理局能以最大化班的可分性减少数据的尺寸。在由食物及药品管理局的特征抽取以后， SOM 能清楚地在输出地图上区分不同状态，监视反常状态能也被采用。田纳西伊斯门(TE ) 过程被采用说明差错诊断和建议方法的监视性能。结果证明与食物及药品管理局方法综合的 SOM 为在复杂化学过程的即时监视和差错诊断有效、有能力。

英文摘要：

Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map （SOM） with Fisher discriminant analysis （FDA） is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman （TE） process is em- ployed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.