Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

作     者：Xiaoyu Jiang Xiangyin Kong Zhiqiang Ge Xiaoyu Jiang;Xiangyin Kong;Zhiqiang Ge

作者机构：the State Key Laboratory of Industrial Control TechnologyCollege of Control Science and EngineeringZhejiang UniversityHangzhou 310027China the Peng Cheng LaboratoryShenzhen 518000China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2023年第10卷第6期

页      面：1445-1461页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0811[工学-控制科学与工程] 

基　　金：supported in part by the National Natural Science Foundation of China(NSFC)(92167106,61833014) Key Research and Development Program of Zhejiang Province(2022C01206)。 

主　　题：Index Terms—Curse of dimensionality data augmentation data-driven modeling industrial processes machine learning 

摘      要：The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh 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 inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.