摩擦电效应
润滑
摩擦学
润滑油
材料科学
接触带电
机械工程
计算机科学
状态监测
工艺工程
信号(编程语言)
压阻效应
国家(计算机科学)
汽车工程
法律工程学
仪表(计算机编程)
静电放电
作者
Sifan Yang,Pengfei Xing,Yijin Sui,Guobin Li
标识
DOI:10.1088/1361-6501/ae0751
摘要
Abstract The monitoring of wear and lubrication states is essential for ensuring the safe operation of mechanical systems. However, conventional techniques often suffer from delayed response and poor resistance to interference. In recent years, triboelectric phenomena—due to their high sensitivity to interfacial conditions—have emerged as a promising approach for real-time monitoring. This review systematically summarizes the fundamental mechanisms of triboelectrification and its recent advances and representative applications in monitoring wear and lubrication states. Four main charge transfer mechanisms—electron, ion, and material transfer, as well as thermoelectric effects—are introduced. The correlation between tribological behavior and triboelectric behavior is further examined based on the electron cloud overlap model. The paper further reviews signal acquisition methods and typical experimental studies, highlighting the relationship between triboelectric signals and wear or lubrication conditions. On the application front, triboelectrification-based monitoring has shown strong adaptability in representative scenarios such as polymer coatings, sealing structures, and rolling bearings. It holds significant promise for lubrication film failure warning, lubrication state identification, intelligent lubrication system development, and lubricant performance evaluation. Nonetheless, challenges remain in distinguishing among different charge generation mechanisms, interpreting complex triboelectric signals, and achieving practical deployment in industrial settings. Future research should focus on experimental designs that clarify and enhance the dominance of specific charge transfer mechanisms. Signal interpretation methods grounded in triboelectrification principles should be further developed. Moreover, the integration of multi-source data with intelligent decision-making systems to advance the engineering and intelligent application of triboelectrification-based monitoring technologies.
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