Data‐based linear Gaussian state‐space model for dynamic process monitoring
Data‐based linear Gaussian state‐space model for dynamic process monitoring
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DOI：
10.1002/aic.13776
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发表时间：
2012-12
期刊：
影响因子：
3.7
通讯作者：
Qiaojun Wen;Zhiqiang Ge;Zhihuan Song
中科院分区：
文献类型：
--
作者：
Qiaojun Wen;Zhiqiang Ge;Zhihuan Song
本文提出了一种基于数据的线性高斯状态空间模型，用于噪声环境下的动态过程监控。引入卡尔曼滤波器构造线性高斯状态空间模型，并采用迭代期望最大化算法进行模型参数学习。结合动态数据信息，提出了一种新的故障检测与识别方法。通过两个实例分析，对新方法中两个监测统计量的可行性和有效性进行了理论分析和验证。此外，详细的故障涂抹效果的分析，所提出的方法提供了与其他识别方法进行比较。基于两个算例的仿真结果，探讨了该方法的优越性。© 2012美国化学工程师学会AIChE J，2012
This article develops a data-based linear Gaussian state-space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state-space model, and an iterative expectation-maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012