Using Digital Twin to Diagnose Faults in Braiding Machinery Based on IoT  

作　　者：Youping Lin Huangbin Lin Dezhi Wei 

机构地区：[1]Chengyi University College,Jimei University,Xiamen,361021,China [2]College of Harbor and Coastal Engineering,Jimei University,Xiamen,361021,China

出　　处：《Intelligent Automation & Soft Computing》2023年第8期1363-1379,共17页智能自动化与软计算（英文）

基　　金：supported by the Fujian Province Natural Science Foundation (Grant No.2019J01711);Fujian ProvinceMiddle-aged Teachers Project (Grant No.JAT210670);Fujian Province Educational Reform Project (Grant No.FBJG2020316).

摘　　要：The digital twin(DT)includes real-time data analytics based on the actual product or manufacturing processing parameters.Data from digital twins can predict asset maintenance requirements ahead of time.This saves money by decreasing operating expenses and asset downtime,which improves company efficiency.In this paper,a digital twin in braiding machinery based on IoT(DTBM-IoT)used to diagnose faults.When an imbalance fault occurs,the system gathers experimental data.After that,the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for defect detection.It is possible to anticipate asset maintenance requirements with DT technology by IoT(Internet of Things)sensors,XR(X-Ray)capabilities,and AI-powered analytics.A DT model’s appropriate design and flexibility remain difficult because of the nonlinear dynamics and unpre-dictability inherent in the degrading process of equipment.The results indicate that the DT in braiding machinery developed allows for precise diagnostic and dynamic deterioration analysis.At least there is 37%growth in efficiency over conventional approaches.

关 键 词：Braiding machinery IOT digital twin defect detection rotor system 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]