Recursive Dynamic inner Principal Component Analysis for Adaptive Process Modeling

被引:1
作者
Qu, Qilin [1 ,2 ]
Dong, Yining [2 ]
Zheng, Ying [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
[2] City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
来源
IFAC PAPERSONLINE | 2024年 / 58卷 / 14期
基金
中国国家自然科学基金;
关键词
Dynamic latent variable methods; Recursive dynamic-inner principal component analysis; Adaptive dynamic process modeling; PCA;
D O I
10.1016/j.ifacol.2024.08.416
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Dynamic latent variable (DLV) methods, represented by dynamic-inner principal component analysis (DiPCA), take into account the high dimensionality and auto-correlation of industrial process data to successfully extract and model the dynamic components. Meanwhile, the time-varying dynamics involved in industrial processes motivate us to explore adaptive DLV methods. In this paper, we propose a recursive DiPCA (RDiPCA) for time-varying dynamic process modeling. Specifically, a recursive autocovariance matrices updating method and the corresponding deflation method are given to achieve low computational costs. The computational efficiency is further improved by a recursive parameter initialization approach in the iterative optimization algorithm solving procedure. Finally, the effectiveness of the proposed algorithm is demonstrated with experiments on a numerical dataset and a wastewater treatment plant dataset.
引用
收藏
页码:682 / 687
页数:6
相关论文
共 14 条
[1]   An Efficient Industrial Big-Data Engine [J].
Basanta-Val, Pablo .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (04) :1361-1369
[2]   Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data [J].
Dong, Yining ;
Liu, Yingxiang ;
Qin, S. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (06) :4068-4076
[3]   A novel dynamic PCA algorithm for dynamic data modeling and process monitoring [J].
Dong, Yining ;
Qin, S. Joe .
JOURNAL OF PROCESS CONTROL, 2018, 67 :1-11
[4]   High-dimensional, slow-time-varying process monitoring technique based on adaptive eigen subspace extraction method [J].
Feng, Xiaowei ;
Kong, Xiangyu ;
He, Chuan ;
Luo, Jiayu .
JOURNAL OF PROCESS CONTROL, 2022, 117 :122-131
[5]   Monitoring of operating point and process dynamics via probabilistic slow feature analysis [J].
Guo, Feihong ;
Shang, Chao ;
Huang, Biao ;
Wang, Kangcheng ;
Yang, Fan ;
Huang, Dexian .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2016, 151 :115-125
[6]   An improved approach for fault detection by simultaneous overcoming of high-dimensionality, autocorrelation, and time-variability [J].
Hajarian, Nastaran ;
Movahedi Sobhani, Farzad ;
Sadjadi, Seyed Jafar .
PLOS ONE, 2020, 15 (12)
[7]  
Hu ZK, 2012, INT J INNOV COMPUT I, V8, P2551
[8]   Disturbance detection and isolation by dynamic principal component analysis [J].
Ku, WF ;
Storer, RH ;
Georgakis, C .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1995, 30 (01) :179-196
[9]  
Li G., 2011, Dynamic latent variable modeling for statistical process monitoring, V44, P12886
[10]   A New Method of Dynamic Latent-Variable Modeling for Process Monitoring [J].
Li, Gang ;
Qin, S. Joe ;
Zhou, Donghua .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2014, 61 (11) :6438-6445