Zhang Chun, Liu Junjie, Shao Yilin, Ni Xingyi, Xie Jiaheng, Luo Hongchun, Yang Tao
School of Science, Xi'an Shiyou University, Xi'an 710065, China.
Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, Lanzhou University, Lanzhou 730000, China.
Sensors (Basel). 2025 Apr 17;25(8):2533. doi: 10.3390/s25082533.
The triboelectric nanogenerator (TENG) is an efficient mechanical energy harvesting device that exhibits excellent performance in the fields of micro-nano energy harvesting and self-powered sensing. In practical application scenarios, it is very important to monitor the speed of rotational machinery in real time. In order to monitor a wider range of rotational speeds, the TENG based on a machine learning algorithm is designed in this paper. The peak power of the TENG reaches a maximum of 6.6 mW and can instantly light up 65 LEDs connected in series. The results show that machine learning can detect speed, greatly improving the speed detection range. The neural network is trained and tested based on the collected electrical signals at different speeds so as to monitor the health of the machine. For the analysis of the collected experimental data, normalization data and a more practical label assignment method of Gaussian soft coding were considered. The study found that after data normalization, the classification prediction accuracy for different speeds is above 0.9, and the prediction results are stable and efficient. Therefore, the machine learning prediction model for speed monitoring proposed by us can be applied to the early warning and monitoring of rotating machinery speed in actual engineering projects.
摩擦纳米发电机（TENG）是一种高效的机械能收集装置，在微纳能量收集和自供电传感领域表现出优异的性能。在实际应用场景中，实时监测旋转机械的转速非常重要。为了监测更广泛的转速范围，本文设计了基于机器学习算法的TENG。TENG的峰值功率最大达到6.6 mW，能够瞬间点亮65个串联的发光二极管。结果表明，机器学习能够检测转速，大大提高了转速检测范围。基于在不同转速下收集的电信号对神经网络进行训练和测试，以监测机器的健康状况。对于收集到的实验数据的分析，考虑了归一化数据和更实用的高斯软编码标签分配方法。研究发现，数据归一化后，不同转速下的分类预测准确率均在0.9以上，预测结果稳定且高效。因此，我们提出的用于转速监测的机器学习预测模型可应用于实际工程项目中旋转机械转速的预警和监测。
