A deep belief network based fault diagnosis model for complex chemical processes
A deep belief network based fault diagnosis model for complex chemical processes
复制标题
基于深度置信网络的复杂化工过程故障诊断模型
DOI：
10.1016/j.compchemeng.2017.02.041
复制
发表时间：
2017-12
影响因子：
4.3
通讯作者：
Jinsong Zhao
中科院分区：
文献类型：
--
作者：
Zhanpeng Zhang;Jinsong Zhao
数据驱动方法被认为是实际化学过程故障检测和诊断（FDD）的理想方法。然而，随着大数据时代的到来，如何有效地提取和呈现故障特征是FDD技术成功产业应用的关键之一。本文提出了一种基于可扩展深度信念网络（DBN）的故障诊断模型。在互信息技术的辅助下，DBN 子网络提取空间和时间域中的单个故障特征。训练全局两层反向传播网络并用于故障分类。在本文的最后部分，利用基准 Tennessee Eastman 过程来说明基于 DBN 的故障诊断模型的性能。
Data-driven methods have been regarded as desirable methods for fault detection and diagnosis (FDD) of practical chemical processes. However, with the big data era coming, how to effectively extract and present fault features is one of the keys to successful industrial applications of FDD technologies. In this paper, an extensible deep belief network (DBN) based fault diagnosis model is proposed. Individual fault features in both spatial and temporal domains are extracted by DBN sub-networks, aided by the mutual information technology. A global two-layer back-propagation network is trained and used for fault classification. In the final part of this paper, the benchmarked Tennessee Eastman process is utilized to illustrate the performance of the DBN based fault diagnosis model.
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影响因子：
2.1
作者：
Jong-Min Lee;S. Qin;In-Beum Lee
通讯作者：
Jong-Min Lee;S. Qin;In-Beum Lee
影响因子：
3.7
作者：
Rato, Tiago;Reis, Marco;De Ketelaere, Bart
通讯作者：
De Ketelaere, Bart
影响因子：
4.2
作者：
Mahadevan, Sankar;Shah, Sirish L.
通讯作者：
Shah, Sirish L.
影响因子：
4.2
作者：
Zhang, Yingwei
通讯作者：
Zhang, Yingwei
DOI：
10.1016/j.compchemeng.2005.06.006
发表时间：
2005-09
期刊：
Comput. Chem. Eng.
影响因子：
--
作者：
Abhijit J. Kulkarni;V. Jayaraman;B. Kulkarni
通讯作者：
Abhijit J. Kulkarni;V. Jayaraman;B. Kulkarni