Xin Chuanfu, Xu Zifeng, Xie Xie, Guo Hengyu, Peng Yan, Li Zhongjie, Liu Lilan, Xie Shaorong
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, P. R. China.
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, 200444, P. R. China.
Adv Sci (Weinh). 2023 Sep;10(26):e2302443. doi: 10.1002/advs.202302443. Epub 2023 Jul 6.
The accomplishment of condition monitoring and intelligent maintenance for cantilever structure-based energy harvesting devices remains a challenge. Here, to tackle the problems, a novel cantilever-structure freestanding triboelectric nanogenerator (CSF-TENG) is proposed, which can capture ambient energy or transmit sensory information. First, with and without a crack in cantilevers, the simulations are carried out. According to simulation results, the maximum change ratios of natural frequency and amplitude are 1.1% and 2.2%, causing difficulties in identifying defects by these variations. Thus, based on Gramian angular field and convolutional neural network, a defect detection model is established to achieve the condition monitoring of the CSF-TENG, and the experimental result manifests that the accuracy of the model is 99.2%. Besides, the relation between the deflection of cantilevers and the output voltages of the CSF-TENG is first built, and then the defect identification digital twin system is successfully created. Consequently, the system is capable of duplicating the operation of the CSF-TENG in a real environment, and displaying defect recognition results, so the intelligent maintenance of the CSF-TENG can be realized.
实现基于悬臂结构的能量收集装置的状态监测和智能维护仍然是一项挑战。在此，为了解决这些问题，提出了一种新型的悬臂结构独立式摩擦纳米发电机（CSF-TENG），它可以捕获环境能量或传输传感信息。首先，对有无裂纹的悬臂梁进行了模拟。根据模拟结果，固有频率和振幅的最大变化率分别为1.1%和2.2%，通过这些变化来识别缺陷存在困难。因此，基于格拉姆角场和卷积神经网络，建立了一个缺陷检测模型来实现CSF-TENG的状态监测，实验结果表明该模型的准确率为99.2%。此外，首先建立了悬臂梁挠度与CSF-TENG输出电压之间的关系，然后成功创建了缺陷识别数字孪生系统。因此，该系统能够在实际环境中复制CSF-TENG的运行，并显示缺陷识别结果，从而实现CSF-TENG的智能维护。
