基于三电纳米发电机的结构裂检测和数字双胞胎示范,用于智能维护
Chuanfu Xin1, Zifeng Xu1,2, Xie Xie1
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, P. R. China.
概括
一个新的 triboelectric 纳米发电机 (TENG) 检测到悬臂结构的裂. 这种状态监测系统达到99.2%的准确性,使能量收集设备的智能维护成为可能.
科学领域:
- 收集能源的技术 收集能源的技术
- 材料科学 材料科学 材料科学
- 结构健康监测 结构健康监测
背景情况:
- 基于悬臂结构的能量采集设备的状态监测和智能维护带来了重大挑战.
- 现有的方法难以检测这些结构中的微妙缺陷.
研究的目的:
- 提出一种新的悬臂结构独立的 triboelectric 纳米发电机 (CSF-TENG) 用于环境能量捕获和传感信息传输.
- 开发一个准确的缺陷检测模型,用于CSF-TENGs的状态监测.
- 建立一个缺陷识别数字双胞胎系统,用于智能维护.
主要方法:
- 在带有裂和没有裂的悬臂上进行了有限元模拟,以分析自然频率和振幅变化.
- 使用格拉米安角场和卷积神经网络开发了一个缺陷检测模型.
- 已经确定了悬臂偏移和CSF-TENG输出电压之间的关系.
- 创建了一个缺陷识别数字双胞胎系统.
主要成果:
- 模拟显示,由于缺陷,自然频率变化很小 (1.1%,振幅2.2%),这表明传统监测的局限性.
- 开发的模型在实验性缺陷检测中实现了99.2%的准确性.
- 成功创建了一个数字双胞胎系统,能够复制CSF-TENG操作并显示缺陷识别.
结论:
- 拟议的CSF-TENG及其相关的AI驱动的监控系统有效地解决了状态监控和智能维护方面的挑战.
- 缺陷检测模型的高精度和数字双胞胎系统的功能为在能源采集应用中可靠的结构健康监测铺平了道路.
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