Multimode Process Monitoring Based on Switching Autoregressive Dynamic Latent Variable Model
Multimode Process Monitoring Based on Switching Autoregressive Dynamic Latent Variable Model
复制标题
基于切换自回归动态潜变量模型的多模式过程监控
DOI：
10.1109/tie.2018.2803727
复制
发表时间：
2018-02
影响因子：
7.7
通讯作者：
Shan Shengdao
中科院分区：
文献类型：
--
作者：
Zhou Le;Zheng Jiaqi;Ge Zhiqiang;Song Zhihuan;Shan Shengdao
在大多数工业过程中，动态特性是非常常见的，在过程控制和监测中应该给予足够的重视。作为一种高阶贝叶斯网络模型，自回归动态潜变量(ARDLV)能够有效地提取动态过程数据中的自相关和互相关。然而，在实际的生产线上，操作条件会频繁变化，这意味着测量不能用单一的稳态模型来描述。本文在概率框架下提出了一组切换ARDLV模型，将原来的单模模型扩展为多模模型。在此基础上，提出了一种适用于多模式过程监控的分层故障检测方法。最后，通过一个数值算例和一个实际的氨合成预碳化装置对所提方法进行了验证。
In most industrials, the dynamic characteristics are very common and should be paid enough attention for process control and monitoring purposes. As a high-order Bayesian network model, autoregressive dynamic latent variable (ARDLV) is able to effectively extract both autocorrelations and cross-correlations in data for a dynamic process. However, the operating conditions will be frequently changed in a real production line, which indicates that the measurements cannot be described using a single steady-state model. In this paper, a set of switching ARDLV models are proposed in the probabilistic framework, which extends the original single model to its multimode form. Based on it, a hierarchical fault detection method is developed for process monitoring in the multimode processes. Finally, the proposed method is demonstrated by a numerical example and a real predecarburization unit in an ammonia synthesis process.
登录
查看更多内容
影响因子：
4.8
作者：
Xie, Lei;Zeng, Jiusun;Gao, Chuanhou
通讯作者：
Gao, Chuanhou
影响因子：
7.7
作者：
Shardt, Yuri A. W.;Hao, Haiyang;Ding, Steven X.
通讯作者：
Ding, Steven X.
影响因子：
7.7
作者：
Li, Gang;Qin, S. Joe;Zhou, Donghua
通讯作者：
Zhou, Donghua
影响因子：
3.7
作者：
Qiaojun Wen;Zhiqiang Ge;Zhihuan Song
通讯作者：
Qiaojun Wen;Zhiqiang Ge;Zhihuan Song
用于非线性过程监控的基于 LWPR 的数据驱动故障检测方法
影响因子：
12.3
作者：
Wang, Guang;Yin, Shen;Kaynak, Okyay
通讯作者：
Kaynak, Okyay