Balasubramanian Vishal, Niksan Omid, Jain Mandeep C, Golovin Kevin, Zarifi Mohammad H
Okanagan MicroElectronics and Gigahertz Applications Laboratory, School of Engineering, Faculty of Applied Science, University of British Columbia, Kelowna, BC, V1V 1V7, Canada.
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.
Nat Commun. 2023 Aug 15;14(1):4916. doi: 10.1038/s41467-023-40636-9.
Unprotected surfaces where a coating has been removed due to erosive wear can catastrophically fail from corrosion, mechanical impingement, or chemical degradation, leading to major safety hazards, financial losses, and even fatalities. As a preventive measure, industries including aviation, marine and renewable energy are actively seeking solutions for the real-time and autonomous monitoring of coating health. This work presents a real-time, non-destructive inspection system for the erosive wear detection of coatings, by leveraging artificial intelligence enabled microwave differential split ring resonator sensors, integrated to a smart, embedded monitoring circuitry. The differential microwave system detects the erosion of coatings through the variations of resonant characteristics of the split ring resonators, located underneath the coating layer while compensating for the external noises. The system's response and performance are validated through erosive wear tests on single- and multi-layer polymeric coatings up to a thickness of 2.5 mm. The system is capable of distinguishing which layer is being eroded (for multi-layer coatings) and estimating the wear depth and rate through its integration with a recurrent neural network-based predictive analytics model. The synergistic combination of artificial intelligence enabled microwave resonators and a smart monitoring system further demonstrates its practicality for real-world coating erosion applications.
因冲蚀磨损导致涂层被去除的未受保护表面，可能会因腐蚀、机械冲击或化学降解而发生灾难性故障，从而导致重大安全隐患、经济损失甚至人员伤亡。作为一种预防措施，包括航空、船舶和可再生能源在内的行业正在积极寻求涂层健康实时自主监测的解决方案。这项工作提出了一种用于涂层冲蚀磨损检测的实时无损检测系统，该系统利用人工智能驱动的微波差分裂环谐振器传感器，并集成到一个智能嵌入式监测电路中。差分微波系统通过位于涂层下方的裂环谐振器谐振特性的变化来检测涂层的侵蚀，同时补偿外部噪声。通过对厚度达2.5毫米的单层和多层聚合物涂层进行冲蚀磨损测试，验证了该系统的响应和性能。该系统能够区分哪一层正在被侵蚀（对于多层涂层），并通过与基于递归神经网络的预测分析模型相结合来估计磨损深度和速率。人工智能驱动的微波谐振器与智能监测系统的协同组合进一步证明了其在实际涂层侵蚀应用中的实用性。
