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

这个i-Corps项目的更广泛的影响/商业潜力是为高性能冷却系统的可靠运行开发一种数据驱动的方法，该方法将突破高功率密度电子和能源系统的热限制。这项拟议的技术不仅将对基础热运输研究产生影响，还将对电动汽车、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、减少能源消耗和减少二氧化碳排放，这项技术将造福于经济和环境。该i-Corps项目基于将高速成像、声发射(AE)和基于深度学习的多模式融合(DL)相结合的高性能两相冷却系统故障检测的准确和可靠技术的开发。该技术具有快速、实时、在线的监测和检测，提供了对数据/图像的极快处理，大约为1-10 ns/帧。创新之处在于使用连接在加热器上的接触式声发射传感器、浸没在工作液中的水听器以及沸腾腔外的高速成像进行非侵入性故障检测。该创新使用了数据驱动的异常识别，通过基于机器学习的降维和多模式信号的融合。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递归神经网络，并通过连接每个通道的输出层来融合信号。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.