THE USE OF NONLINEAR DYNAMIC SYSTEM AND DEEP LEARNING IN PRODUCTION CONDITION MONITORING AND PRODUCT QUALITY PREDICTION

Jun Li, Ruoqi Wang, Mohammed Sh. Alhodaly (), Jingfeng Huang and Lin Qi
Additional contact information
Jun Li: School of Information and Intelligent Engineering, Zhejiang Wanli University, Ningbo City 315100, P. R. China
Ruoqi Wang: School of Information and Intelligent Engineering, Zhejiang Wanli University, Ningbo City 315100, P. R. China
Mohammed Sh. Alhodaly: ��Nonlinear Analysis and Applied Mathematics (NAAM)-Research Group, Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia
Jingfeng Huang: ��Department of Scientific Research, Zhejiang Wanli University, Ningbo City 315100, P. R. China
Lin Qi: ��Department of Scientific Research, Zhejiang Wanli University, Ningbo City 315100, P. R. China

FRACTALS (fractals), 2022, vol. 30, issue 02, 1-13

Abstract: To improve the product quality and production efficiency in industrial production, the industrial production process is explored. First, for the production state in industrial production, the radio frequency identification technology (RFID) is used to monitor the state of the nodes in the production process and collect data. Based on the problem of blind area in industrial production, an adaptive controller design based on uncertain nonlinear dynamic systems is introduced; second, based on this, for the problem that product quality is difficult to guarantee in the process flow, the improved multi-layer feedforward network (back propagation, BP) algorithm is used to predict it. Finally, the control diagnosis algorithm based on multi-order least squares support vector machine (LS-SVM) is used to analyze the abnormal cause of the product. The results show that the use of the RFID can monitor the production process in real time and obtain real-time product quality information data. The proposed adaptive controller based on uncertain nonlinear dynamic system has good applicability in solving the problem of blind area. The improved BP neural network algorithm improves the prediction accuracy in the process of product quality prediction and increases the convergence speed of the algorithm. The control diagnosis algorithm based on multi-order least squares that support vector machine (SVM) can classify and learn the historical data of products, as well as perform multi-order search and analysis on the causes of abnormal quality. It helps relevant personnel to carry out process improvement and control, thereby achieving the purpose of improving product quality. Through this investigation, it is found that the method proposed can monitor the state of industrial production in real time and help relevant personnel to control and predict product quality. It can be applied in actual processes.

Keywords: Radio Frequency Identification Technology; Uncertain Nonlinear Dynamic System; Least Squares Support Vector Machine; BP Neural Network Algorithm; Quality Control (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X22400680
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:30:y:2022:i:02:n:s0218348x22400680

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X22400680

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().