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Volume 16, Issue 4
Real-Time Monitoring of Knitting Machine Performance Using IoT and Machine Learning: Innovations and Applications

Sherien Elkateb, Ahmed Métwalli & Abdelrahman Shendy

Journal of Fiber Bioengineering & Informatics, 16 (2023), pp. 297-309.

Published online: 2024-09

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  • Abstract

Textile technologies are revolutionising with Industry 4.0. This research aims to introduce a novel real-time monitoring system in the knitting sector using the Internet of Things and machine learning technologies to measure and display productivity precisely through an interactive dashboard. Sensors were integrated into a circular knitting machine to track productivity and performance. A comparative statistical analysis through three processing phases demonstrates the high accuracy and precision of the current system, as evidenced by minimum variance and error values. The t-test results validate a non-significant difference between actual and device-measured production. Thus, it enables real-time monitoring, preventive maintenance, and cost-effective quality in knitting machines.

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@Article{JFBI-16-297, author = {Elkateb , SherienMétwalli , Ahmed and Shendy , Abdelrahman}, title = {Real-Time Monitoring of Knitting Machine Performance Using IoT and Machine Learning: Innovations and Applications}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2024}, volume = {16}, number = {4}, pages = {297--309}, abstract = {

Textile technologies are revolutionising with Industry 4.0. This research aims to introduce a novel real-time monitoring system in the knitting sector using the Internet of Things and machine learning technologies to measure and display productivity precisely through an interactive dashboard. Sensors were integrated into a circular knitting machine to track productivity and performance. A comparative statistical analysis through three processing phases demonstrates the high accuracy and precision of the current system, as evidenced by minimum variance and error values. The t-test results validate a non-significant difference between actual and device-measured production. Thus, it enables real-time monitoring, preventive maintenance, and cost-effective quality in knitting machines.

}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim03031}, url = {http://global-sci.org/intro/article_detail/jfbi/23426.html} }
TY - JOUR T1 - Real-Time Monitoring of Knitting Machine Performance Using IoT and Machine Learning: Innovations and Applications AU - Elkateb , Sherien AU - Métwalli , Ahmed AU - Shendy , Abdelrahman JO - Journal of Fiber Bioengineering and Informatics VL - 4 SP - 297 EP - 309 PY - 2024 DA - 2024/09 SN - 16 DO - http://doi.org/10.3993/jfbim03031 UR - https://global-sci.org/intro/article_detail/jfbi/23426.html KW - Internet of Things, machine learning, system accuracy, real-time monitoring, knitting machine. AB -

Textile technologies are revolutionising with Industry 4.0. This research aims to introduce a novel real-time monitoring system in the knitting sector using the Internet of Things and machine learning technologies to measure and display productivity precisely through an interactive dashboard. Sensors were integrated into a circular knitting machine to track productivity and performance. A comparative statistical analysis through three processing phases demonstrates the high accuracy and precision of the current system, as evidenced by minimum variance and error values. The t-test results validate a non-significant difference between actual and device-measured production. Thus, it enables real-time monitoring, preventive maintenance, and cost-effective quality in knitting machines.

Elkateb , SherienMétwalli , Ahmed and Shendy , Abdelrahman. (2024). Real-Time Monitoring of Knitting Machine Performance Using IoT and Machine Learning: Innovations and Applications. Journal of Fiber Bioengineering and Informatics. 16 (4). 297-309. doi:10.3993/jfbim03031
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