Katsidimas Ioannis, Kostopoulos Vassilis, Kotzakolios Thanasis, Nikoletseas Sotiris E, Panagiotou Stefanos H, Tsakonas Constantinos
Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece.
Mechanical Engineering and Aeronautics Department, University of Patras, 26504 Patras, Greece.
Sensors (Basel). 2023 Jan 12;23(2):896. doi: 10.3390/s23020896.
Recent advances both in hardware and software have facilitated the embedded intelligence (EI) research field, and enabled machine learning and decision-making integration in resource-scarce IoT devices and systems, realizing "conscious" and self-explanatory objects (smart objects). In the context of the broad use of WSNs in advanced IoT applications, this is the first work to provide an extreme-edge system, to address structural health monitoring (SHM) on polymethyl methacrylate (PPMA) thin-plate. To the best of our knowledge, state-of-the-art solutions primarily utilize impact positioning methods based on the time of arrival of the stress wave, while in the last decade machine learning data analysis has been performed, by more expensive and resource-abundant equipment than general/development purpose IoT devices, both for the collection and the inference stages of the monitoring system. In contrast to the existing systems, we propose a methodology and a system, implemented by a low-cost device, with the benefit of performing an online and on-device impact localization service from an agnostic perspective, regarding the material and the sensors' location (as none of those attributes are used). Thus, a design of experiments and the corresponding methodology to build an experimental time-series dataset for impact detection and localization is proposed, using ceramic piezoelectric transducers (PZTs). The system is excited with a steel ball, varying the height from which it is released. Based on TinyML technology for embedding intelligence in low-power devices, we implement and validate random forest and shallow neural network models to localize in real-time (less than 400 ms latency) any occurring impacts on the structure, achieving higher than 90% accuracy.
硬件和软件方面的最新进展推动了嵌入式智能（EI）研究领域的发展，并实现了机器学习与决策在资源稀缺的物联网设备和系统中的集成，从而实现了具有“意识”且能自我解释的对象（智能对象）。在无线传感器网络（WSN）广泛应用于先进物联网应用的背景下，本文首次提出了一种极端边缘系统，用于解决聚甲基丙烯酸甲酯（PPMA）薄板的结构健康监测（SHM）问题。据我们所知，目前的先进解决方案主要利用基于应力波到达时间的冲击定位方法，而在过去十年中，机器学习数据分析是通过比通用/开发用途的物联网设备更昂贵且资源更丰富的设备进行的，用于监测系统的数据收集和推理阶段。与现有系统不同，我们提出了一种由低成本设备实现的方法和系统，其优点是从不可知的角度在线且在设备上执行冲击定位服务，而无需考虑材料和传感器的位置（因为不使用这些属性）。因此，本文提出了一种实验设计和相应的方法，用于使用陶瓷压电传感器（PZT）构建用于冲击检测和定位的实验时间序列数据集。该系统使用钢球进行激励，并改变钢球释放的高度。基于用于在低功耗设备中嵌入智能的TinyML技术，我们实现并验证了随机森林和浅层神经网络模型，以实时（延迟小于400毫秒）定位结构上发生的任何冲击，准确率超过90%。
