I-Corps: Non-Intrusive Cooling System Fault Detection Using Deep Learning of Acoustic Emissions

I-Corps项目更广泛的影响/商业潜力是开发一种数据驱动的方法，用于高性能冷却系统的可靠运行，这将突破高功率密度电子和能源系统的热限制。所提出的技术不仅将对基础热输运研究产生影响，而且将对电动汽车、混合动力汽车、电力电子、发电等工业生产产生影响。通过实现更高效的冷却，降低能源消耗，减少二氧化碳排放，这项技术将有利于经济和环境。I-Corps项目的基础是开发准确可靠的高性能两相冷却系统故障检测技术，将高速成像、声发射（AE）与使用深度学习（DL）的多模态融合相结合。该技术具有快速，实时，在线监测和检测，提供极快的数据/图像处理，在1-10纳秒/帧的顺序。该技术的创新之处在于，采用连接在加热器上的接触式声发射传感器、浸入工作流体中的水听器以及在沸腾室外的高速成像技术，进行非侵入式故障检测。该创新技术通过基于机器学习的降维和多模态信号融合，使用数据驱动的异常识别。我们的技术将为每种信号模式利用优化的模型架构，例如，用于图像的卷积神经网络，用于时间序列信号的循环神经网络，并通过连接每个通道的输出层来融合信号。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a data-driven approach for reliable operations of high-performance cooling systems that will break through the thermal limitations for high-power-density electronic and energy systems. The proposed technology will not only make an impact on fundamental thermal transport research but also industrial production of electric vehicles, hybrid vehicles, power electronics, power generation, etc. By enabling more efficient cooling, reduced energy consumption, and reduced carbon oxide emissions, this technology will benefit both the economy and the environment.This I-Corps project is based on the development of accurate and reliable technology for fault detection of high-performance two-phase cooling systems by coupling high-speed imaging, acoustic emission (AE), with multimodal fusion using deep learning (DL). The technology has fast, real-time, online monitoring and detection providing extremely fast processing of data/images, in the order of 1-10 ns/frame. The innovation is non-invasive fault detection using contact AE sensor attached to the heater, hydrophones immersed in the working fluid, and high-speed imaging outside the boiling chamber. The innovation uses data-driven anomaly identification via machine learning-based dimensionality reduction, and fusion of multimodal signals. Our technology will leverage optimized model architectures for each modality of signals, e.g., convolutional neural networks for images, recurrent neural networks for time-series signals, and fuse the signals by concatenating the output layer of each channel.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.