@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 = {<p style="text-align: justify;">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.</p>},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim03031},
url = {http://global-sci.org/intro/article_detail/jfbi/23426.html}
}