Study on fault diagnosis model and chemical process fault diagnosis based on improved KFDA and DE optimized SOM

被引：0

作者：

Li G. ^{[1
]}

Zhang X. ^{[1
]}

Cai S. ^{[1
]}

Jia Y. ^{[1
]}

Ning Z. ^{[1
]}

机构：

[1] Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao

来源：

Huagong Jinzhan/Chemical Industry and Engineering Progress | 2022年 / 41卷 / 04期

关键词：

Differential evolution algorithm; Fault diagnosis; Neural network; Optimization design; Process control;

D O I：

10.16085/j.issn.1000-6613.2021-0821

中图分类号：

学科分类号：

摘要：

Due to the high dimension of fault diagnosis data in chemical process, the fault features are not easy to distinguish, and the SOM network is easy to fall into the problem of local best. A fault diagnosis method based on KFDA and DE algorithm to optimize SOM neural network was proposed. Firstly, Euclidean distance was used to weight the distance between classes, so as to avoid the problem of overlapping of projected data due to the large distance between classes. As a consequence, the fault data samples could obtain better projection effect and optimize the classification performance. Then, the DE algorithm was used to dynamically adjust the weight vectors of SOM neural network, which effectively avoids the problem of falling into local optimum due to the appearance of "dead neurons". The fault data of TE process and PX disproportionation process were tested. The results showed that compared with traditional SOM network, KFDA-DE-SOM algorithm has higher classification diagnosis accuracy and can be effectively applied to the fault diagnosis of the chemical process. © 2022, Chemical Industry Press Co., Ltd. All right reserved.

引用

页码：1793 / 1801

页数：8

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