A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
复制标题
一种基于深度学习的新型化学过程故障诊断方法，具有扩展的深度信念网络
DOI：
10.1016/j.isatra.2019.07.001
复制
发表时间：
2020-01-01
期刊：
影响因子：
7.3
通讯作者：
Gui, Weihua
中科院分区：
文献类型：
--
作者：
Wang, Yalin;Pan, Zhuofu;Gui, Weihua
深度学习网络最近被用于故障检测和诊断（FDD），因为它可以有效地处理通常具有高非线性和强相关性的工业过程数据。然而，原始数据中的有价值信息可以通过传统深度网络中的逐层特征压缩来过滤。这不利于后续故障分类的微调阶段。为了缓解这个问题，提出了扩展深度置信网络（EDBN）来充分利用原始数据中的有用信息，其中原始数据与隐藏特征相结合，作为预训练阶段每个扩展受限玻尔兹曼机（ERBM）的输入。然后，考虑过程数据的动态特性，构建基于动态EDBN的故障分类器。最后，为了测试所提方法的性能，将其应用于田纳西伊士曼（TE）过程进行故障分类。通过比较不同网络结构下的EDBN和DBN，结果表明EDBN比传统DBN具有更好的特征提取和故障分类性能。 (C) 2019 年 ISA。由爱思唯尔有限公司出版。保留所有权利。
Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its effectiveness in handling industrial process data, which are often with high nonlinearities and strong correlations. However, the valuable information in the raw data may be filtered with the layer-wise feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is proposed to fully exploit useful information in the raw data, in which raw data is combined with the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic characteristics of process data into consideration. Finally, to test the performance of the proposed method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.