Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing

Chuan-Pin Lu; Yan-Long Huang; Po-Jen Lai

doi:10.46604/ijeti.2023.11158

Authors

Chuan-Pin Lu Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan, ROC
Yan-Long Huang Department of Information Technology, Meiho University, Pingtung, Taiwan, ROC
Po-Jen Lai Department of Information Technology, Meiho University, Pingtung, Taiwan, ROC

DOI:

https://doi.org/10.46604/ijeti.2023.11158

Keywords:

abnormal yarn tension, deep learning, edge computing, twisted yarn, long short-term memory

Abstract

This study aims to develop an artificial intelligence module for recognizing abnormal tension in textile weaving, The module can be used to address the time-consuming and inaccurate issues associated with traditional manual methods. Long short-term memory (LSTM) recurrent neural networks as the algorithm for identifying different types of abnormal tension are employed in this module. This study focuses on training and validating the model using five common patterns. Additionally, an approach involving the integration of plug-in modules and edge computing in deep learning is employed to achieve the research objectives without altering the original system architecture. Multiple experiments were conducted to search for the optimal model parameters. According to the experimental results, the average recognition rate for abnormal tension is 97.12%, with an average computation time of 46.2 milliseconds per sample. The results indicate that the recognition accuracy and computation time meet the practical performance requirements of the system.

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