Integrated Systems Laboratory, ETH Zurich, 8092 Zurich, Switzerland.
DEI, University of Bologna, 40126 Bologna, Italy.
Sensors (Basel). 2019 Jun 19;19(12):2747. doi: 10.3390/s19122747.
We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade's wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.
我们报告了一种基于自持续无线加速度计的系统，用于带锯片的磨损检测。由于低功耗硬件设计、小型热电发电机（TEG）热能收集、超低功耗唤醒无线电、电源管理和低复杂度算法的结合，我们的解决方案可以永久运行，同时实现高精度。板载算法处理传感器数据，提取特征，执行刀片磨损检测所需的分类，并通过无线方式发送报告。在真实部署场景中的实验结果表明，其准确性可与在 PC 上执行的最先进算法相媲美，并展示了使用小型热电发电机进行能量收集的解决方案的能量中性。分析了节点上实施的各种低功耗技术的影响，突出了板载处理、纳功率唤醒无线电以及收集和低功耗设计相结合的优势。最后，精确的现场能量摄入测量，加上模拟，证明了所提出的方法是能量自主的，可以永久运行。
