用于航空发动机齿轮故障监测的自驱动振动传感器

Self-driven vibration sensor for monitoring gear faults in aircraft engine

目前传统振动监测手段受限于航空发动机高温环境影响,导致振动传感器探测精度难以进一步提升。鉴于此,本文设计了一种基于摩擦电效应和压电效应的自驱动振动传感器,通过对敏感层材料配比的合理选择,实现电学输出性能与高温输出稳定性的兼顾。在最佳振动条件下,摩擦电器件的输出可达5.1V和0.42μA,而压电器件输出可达1.8V和0.3μA。基于自驱动传感器优异的电学输出性能,在机器学习算法的帮助下,开发的自驱动振动传感器可对齿轮的点蚀故障以及断齿故障进行精准识别,其识别准确率高达98.2%。实验证明本文开发的自驱动振动传感器能够有效监测航空发动机齿轮的运行状态,为航空发动机智能化健康管理提供了重要的参考价值。

However,traditional vibration monitoring methods are constrained by the high-temperature environment of aircraft engines,which results in difficulty of enhancement of vibration sensor detection precision.So,a self-powered vibration sensor based on triboelectric and piezoelectric effects is proposesd.By selecting an optimal ratio of sensitive layer materials,a balance between electrical output performance and stability in high-temperature output is achieved.Under optimal vibration conditions,output of triboelectric device 5.1 V and 0.42μA,whereas piezoelectric device is 1.8 V and 0.3μA.Based on the superior electrical output of property self-powered sensor and with the help of machine learning algorithms,the developed sensor can accurately identify gear faults such as pitting and tooth breakage,achieving an accuracy of up to 98.2%.Experimental results demonstrate that the developed self-powered vibration sensor can effectively monitor the operational status of aircraft gears,offering critical reference value for intelligent engine health management of aircraft engine.