Citation

Choo, Kan Yeep and Ngo, Hong Yeap and Abdul Aziz, Nurul Ain and Jabidin, Hafiz Zulhazmi and Tee, Connie and Muniandy, Sithi Vinayakam and Ibrahim@Ghazali, Siti Azlida and Abdul Rashid, Hairul Azhar and Mokhtar, Mohd Ridzuan and Zan, Mohd Saiful Dzulkefly (2024) Machine Learning-Based Classification of Hand Tool Vibrations Using Distributed Fiber Optic Sensors for Road Health Monitoring. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

Text Machine Learning-Based Classification of Hand Tool Vibrations Using Distributed Fiber Optic Sensors for Road Health Monitoring.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Distributed acoustic optical fiber sensing is recently considered a promising technique in road health monitoring compared to conventional transducer technology. However, the huge amount of sensor data makes classifying vibrations that affect road health conditions time-consuming, labor-intensive, and prone to human errors. In this work, we proposed an intelligent optical fiber road health monitoring system using machine learning techniques to mitigate the drawbacks. This is implemented by detecting the vibration signals produced by various hand tools with a distributed acoustic sensing interrogator unit. A support vector machine is trained with the signal, power spectrum, and frequency component features of the vibration signals. The vibration patterns of various hand tools are successfully classified and a considerable well accuracy close to 80% is achieved. Thus, this work demonstrated the possibilities and capabilities of intelligent road health monitoring using machine learningbased distributed optical fiber sensors.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Fiber optic sensor, distributed acoustic sensing, machine learning, Support Vector Machine (SVM), road health monitoring.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	12 Feb 2025 00:42
Last Modified:	12 Feb 2025 00:43
URII:	http://shdl.mmu.edu.my/id/eprint/13415

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