Yule Lawrence, Harris Nicholas, Hill Martyn, Zaghari Bahareh, Grundy Joanna
Smart Electronic Materials and Systems Research Group, School of Electronics and Computer Science, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK.
Mechatronics Research Group, School of Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK.
Sensors (Basel). 2024 Feb 7;24(4):1081. doi: 10.3390/s24041081.
This paper addresses the challenging issue of achieving high spatial resolution in temperature monitoring of printed circuit boards (PCBs) without compromising the operation of electronic components. Traditional methods involving numerous dedicated sensors such as thermocouples are often intrusive and can impact electronic functionality. To overcome this, this study explores the application of ultrasonic guided waves, specifically utilising a limited number of cost-effective and unobtrusive Piezoelectric Wafer Active Sensors (PWAS). Employing COMSOL multiphysics, wave propagation is simulated through a simplified PCB while systematically varying the temperature of both components and the board itself. Machine learning algorithms are used to identify hotspots at component positions using a minimal number of sensors. An accuracy of 97.6% is achieved with four sensors, decreasing to 88.1% when utilizing a single sensor in a pulse-echo configuration. The proposed methodology not only provides sufficient spatial resolution to identify hotspots but also offers a non-invasive and efficient solution. Such advancements are important for the future electrification of the aerospace and automotive industries in particular, as they contribute to condition-monitoring technologies that are essential for ensuring the reliability and safety of electronic systems.
本文探讨了在不影响电子元件运行的情况下，实现印刷电路板（PCB）温度监测高空间分辨率这一具有挑战性的问题。涉及众多专用传感器（如热电偶）的传统方法往往具有侵入性，可能会影响电子功能。为克服这一问题，本研究探索了超声导波的应用，具体使用了数量有限、经济高效且不具侵入性的压电晶片有源传感器（PWAS）。利用COMSOL多物理场软件，在简化的印刷电路板中模拟波传播，同时系统地改变元件和电路板本身的温度。使用机器学习算法，通过最少数量的传感器识别元件位置的热点。四个传感器的准确率达到97.6%，在脉冲回波配置中使用单个传感器时，准确率降至88.1%。所提出的方法不仅提供了足够的空间分辨率来识别热点，还提供了一种非侵入性且高效的解决方案。这些进展对于航空航天和汽车行业未来的电气化尤为重要，因为它们有助于实现对确保电子系统可靠性和安全性至关重要的状态监测技术。
