Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

doi:10.1109/JAS.2023.123396

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality
	Xiaoyu Jiang; Xiangyin Kong; Zhiqiang Ge
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2023
卷号	10 期号:6 页码:1445-1461
摘要	The curse of dimensionality refers to the problem of increased sparsity and computational complexity when dealing with high-dimensional data. In recent years, the types and variables of industrial data have increased significantly, making data-driven models more challenging to develop. To address this problem, data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensional industrial data. This paper systematically explores and discusses the necessity, feasibility, and effectiveness of augmented industrial data-driven modeling in the context of the curse of dimensionality and virtual big data. Then, the process of data augmentation modeling is analyzed, and the concept of data boosting augmentation is proposed. The data boosting augmentation involves designing the reliability weight and actual-virtual weight functions, and developing a double weighted partial least squares model to optimize the three stages of data generation, data fusion, and modeling. This approach significantly improves the interpretability, effectiveness, and practicality of data augmentation in the industrial modeling. Finally, the proposed method is verified using practical examples of fault diagnosis systems and virtual measurement systems in the industry. The results demonstrate the effectiveness of the proposed approach in improving the accuracy and robustness of data-driven models, making them more suitable for real-world industrial applications.
关键词	Curse of dimensionality data augmentation data-driven modeling industrial processes machine learning
DOI	10.1109/JAS.2023.123396
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/51681
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Xiaoyu Jiang,Xiangyin Kong,Zhiqiang Ge. Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(6):1445-1461.
APA	Xiaoyu Jiang,Xiangyin Kong,&Zhiqiang Ge.(2023).Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality.IEEE/CAA Journal of Automatica Sinica,10(6),1445-1461.
MLA	Xiaoyu Jiang,et al."Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality".IEEE/CAA Journal of Automatica Sinica 10.6(2023):1445-1461.

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