Institutional Repository of Coll Elect & Informat Engn
Nonlinear Quality-Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process | |
Liu, Kai1,2; Zhao, Xiaoqiang1,2,3; Hui, Yongyong1,2,3; Jiang, Hongmei1,2,3 | |
2024-03-30 | |
在线发表时间 | 2024-03 |
发表期刊 | CHEMICAL ENGINEERING & TECHNOLOGY |
ISSN | 0930-7516 |
摘要 | Quality-related fault detection has become a hot research topic in recent years. It is not reliable to measure quality-related relationships only by mutual information among process variables and single-quality variables. Frequent alarms for quality-unrelated faults seriously affect the normal operation of industrial production. At the same time, the strong nonlinearity of the process data leads to the difficulty of feature extraction. In this paper, we propose a fault detection algorithm based on nonlinear quality-related neighborhood embedding neural orthogonal mapping (QR-NENOM). First, quality-related and quality-unrelated variables are selected by Bayesian fusion mutual information, and the weighted method of mutual information is used to enhance the quality-related information and suppress the quality-unrelated information. Second, local manifold information is obtained by reconstructing nearest neighbors of process data, and key features are extracted by the nonlinear method composed of neural network and orthogonal mapping. Then, statistical indicators are established to complete fault detection. Finally, the nonlinear feature extraction ability of NENOM is verified by numerical examples, and the QR-NENOM algorithm proposed in this paper is applied to the penicillin fermentation process. Comparative experiments show that QR-NENOM has better detection performance for quality-related faults and fewer alarms for quality-unrelated faults. The QR-NENOM algorithm not only has a high fault detection rate for quality-related faults, but also has fewer alarms for unrelated faults. Moreover, it preserves the local flow structure information while extracting the key nonlinear features of the data. image |
关键词 | Batch process Fault detection Neighborhood preserving embedding Neural network Quality related |
DOI | 10.1002/ceat.202200577 |
收录类别 | SCIE ; EI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China; College Industrial Support Project of Gansu Province [2023CYZC-24]; Open Fund project of Gansu Provincial Key Laboratory of Advanced Control for Industrial Process [2022KX07]; [62263021] |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Chemical |
WOS记录号 | WOS:001193520900001 |
出版者 | WILEY-V C H VERLAG GMBH |
EI入藏号 | 20241415831682 |
EI主题词 | Embeddings |
EI分类号 | 723.2 Data Processing and Image Processing ; 723.4 Artificial Intelligence ; 802.3 Chemical Operations |
原始文献类型 | Article;Early Access |
EISSN | 1521-4125 |
引用统计 | 无
|
文献类型 | 期刊论文 |
条目标识符 | https://ir.lut.edu.cn/handle/2XXMBERH/170207 |
专题 | 电气工程与信息工程学院 |
通讯作者 | Zhao, Xiaoqiang |
作者单位 | 1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Qilihe St, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Gansu Key Lab Adv Control Ind Proc, Qilihe St, Lanzhou 730050, Peoples R China; 3.Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Qilihe St, Lanzhou 730050, Peoples R China |
第一作者单位 | 电气工程与信息工程学院; 兰州理工大学 |
通讯作者单位 | 电气工程与信息工程学院; 兰州理工大学 |
第一作者的第一单位 | 电气工程与信息工程学院 |
推荐引用方式 GB/T 7714 | Liu, Kai,Zhao, Xiaoqiang,Hui, Yongyong,et al. Nonlinear Quality-Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process[J]. CHEMICAL ENGINEERING & TECHNOLOGY,2024. |
APA | Liu, Kai,Zhao, Xiaoqiang,Hui, Yongyong,&Jiang, Hongmei.(2024).Nonlinear Quality-Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process.CHEMICAL ENGINEERING & TECHNOLOGY. |
MLA | Liu, Kai,et al."Nonlinear Quality-Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process".CHEMICAL ENGINEERING & TECHNOLOGY (2024). |
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