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<p>Polymer gears are increasingly vital in modern industrial applications. However, existing standards and monitoring techniques lack the precision required to track real-time wear at the individual tooth level. Conventional methods primarily rely on indirect mechanical responses that often lack sufficient sensitivity. This study introduces a novel approach utilizing triboelectrification to detect polymer gear wear. Since triboelectric signals sensitively reflect surface interactions, they offer a promising non-contact pathway for accurate progression monitoring. By integrating charge-based sensing with vibration analysis, this research provides a robust framework for real-time wear detection. This study identifies three distinct gear life phases: running-in, steady-state, and failure. Increased loads significantly accelerate these transitions. A 15&#xa0;kg load reduces service life by 53.33% compared to a 10&#xa0;kg load. Induced current monitoring validates wear progression and demonstrates superior sensitivity over vibration data. This method detects failure up to 3,000&#xa0;s earlier than vibration monitoring. Failure mechanisms involve tooth chipping and structural breakage. Surface height decreases from 680&#xa0;μm during steady-state to 322&#xa0;μm during failure. Finally, repeated bending stress triggers crack initiation at the tooth root, which leads to complete fracture. These findings contribute to sustainable manufacturing by enabling precise predictive maintenance, reducing material waste, and enhancing the energy efficiency of mechanical transmission systems in precision machinery. Consequently, this work supports sustainable development goals by fostering resource-efficient industrial infrastructure.</p>

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Triboelectric detection method applied to life phase identification and failure mechanism analysis of polymer gears

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Chil-Chyuan Kuo,
  • Huezaimie Nizam,
  • Armaan Farooqui,
  • Song-Hua Huang

摘要

Polymer gears are increasingly vital in modern industrial applications. However, existing standards and monitoring techniques lack the precision required to track real-time wear at the individual tooth level. Conventional methods primarily rely on indirect mechanical responses that often lack sufficient sensitivity. This study introduces a novel approach utilizing triboelectrification to detect polymer gear wear. Since triboelectric signals sensitively reflect surface interactions, they offer a promising non-contact pathway for accurate progression monitoring. By integrating charge-based sensing with vibration analysis, this research provides a robust framework for real-time wear detection. This study identifies three distinct gear life phases: running-in, steady-state, and failure. Increased loads significantly accelerate these transitions. A 15 kg load reduces service life by 53.33% compared to a 10 kg load. Induced current monitoring validates wear progression and demonstrates superior sensitivity over vibration data. This method detects failure up to 3,000 s earlier than vibration monitoring. Failure mechanisms involve tooth chipping and structural breakage. Surface height decreases from 680 μm during steady-state to 322 μm during failure. Finally, repeated bending stress triggers crack initiation at the tooth root, which leads to complete fracture. These findings contribute to sustainable manufacturing by enabling precise predictive maintenance, reducing material waste, and enhancing the energy efficiency of mechanical transmission systems in precision machinery. Consequently, this work supports sustainable development goals by fostering resource-efficient industrial infrastructure.