Markovian and Non-Markovian sensitivity enhancing transformations for process monitoring
Markovian and Non-Markovian sensitivity enhancing transformations for process monitoring
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DOI：
10.1016/j.ces.2017.01.047
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发表时间：
2017-05
影响因子：
4.7
通讯作者：
Tiago J. Rato;M. Reis
中科院分区：
文献类型：
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作者：
Tiago J. Rato;M. Reis
过程监控是现代工业过程中的一项关键活动。尽管利用变量正常工况关联的因果关联模型可以相当有效地进行异常检测，但故障诊断和故障排除确实需要因果信息。在本文中，我们提出了一种新的插件方法，该方法将因果网络结构引入基于Hotelling ' st2方法的经典监测方案中。与一个知名的监控方案相关联的模块化插件特性，旨在促进在故障分析和诊断中使用有关系统结构的更多信息的好处。预处理模块由一个灵敏度增强转换（SET）组成，该转换结合了从正常运行数据推断的网络结构，最近在监测工业过程的相关结构方面取得了重大进展。此外，我们在SET的开发中考虑了马尔可夫和非马尔可夫网络结构。提出的方法通过两个模拟案例研究（CSTR和田纳西伊士曼基准）进行了测试，并与几种替代方法进行了比较。获得的结果建议使用静态非马尔可夫SET作为Hotelling ' st2方法的预处理。
Process monitoring is a key activity in modern industrial processes. Even though abnormality detection can be rather effectively done with resort to acausal correlation models of the variables normal operating conditions associations, fault diagnosis and troubleshooting do require causal information. In this article, we propose a new plug-in approach that brings the causal network structure into a classical monitoring scheme based on the Hotelling’sT2methodology. The modular plug-in nature associated to a well-known monitoring scheme aims at facilitating the access to the benefits of using more information about the system structure in fault analysis and diagnosis. The pre-processing module consists of a Sensitivity Enhancing Transformation (SET) that incorporates the network structure inferred from normal operation data, which has recently conducted to significant improvements for monitoring the correlation structure of industrial processes. Additionally, we consider both Markovian and Non-Markovian network structures in the development of the SET. The proposed methodology was tested with two simulated case studies (a CSTR and the Tennessee Eastman benchmark) and compared with several alternative approaches. The results obtained recommend the use of the static Non-Markovian SET as pre-processing for the Hotelling’sT2methodology.