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

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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.