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All Journal Mechanical Engineering for Society and Industry
Hanif Fakhrurroja
Research Center for Smart Mechatronics, BRIN, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Smart vibration sensing and predictive analytics for intelligent textile manufacturing: An IoT-edge and machine learning method Deni Kurnia; Agus Sutanto; Hanif Fakhrurroja; Lovely Son
Mechanical Engineering for Society and Industry Vol 5 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.13588

Abstract

This study aimed to propose an IoT-Edge method for detecting vibration abnormalities in Textile Manufacturing, specifically on Draw Texturing Yarn (DTY) machines using an ADXL345 sensor and a Machine Learning Algorithm. The proposed system incorporated wireless sensor nodes, the MQTT protocol, Fast Fourier Transform (FFT) analysis, and a tuned Random Forest (RF) classifier to enable real-time monitoring as well as predictive maintenance. During the analysis, vibration data were collected from 13 spindles, with features extracted in both time as well as frequency domains to distinguish between normal and abnormal machine conditions. The RF model, optimized through hyperparameter tuning, achieved an accuracy of 97%, significantly outperforming the Support Vector Machine (SVM) baseline, which reached 71%. Major results showed the effectiveness of energy and centroid features in fault detection, with the Z-axis vibration proving to be a good indicator of yarn defects. The system presented low latency (average 20.37 ms) in data transmission using the MQTT protocol, ensuring practical deployability. This study offered a scalable and cost-effective solution for industrial vibration monitoring, bridging gaps in real-time processing and seamless IoT incorporation to support predictive maintenance in textile manufacturing.
Co-Authors Agus Sutanto Deni Kurnia Lovely Son