Abstract: A computer-implemented method, system, and computer program product for detecting or predicting the occurrence of critical heat flux in cooling systems. A model is built and trained to detect or predict the occurrence of critical heat flux in a cooling system based on acoustic data and/or image data. Such a model may be utilized to detect or predict the occurrence of critical heat flux in a cooling system by converting acoustic signals received from sensors remotely located from the cooling system from the temporal domain to a frequency domain. Furthermore, such a model may be utilized to detect or predict the occurrence of critical heat flux in a cooling system by extracting features from image data and acoustic data obtained from sensors remotely located from the cooling system and then concatenating such features.

Abstract: A computer-implemented method, system and computer program product for detecting or predicting system faults in cooling systems. A model (deep learning model) is built and trained to detect or predict system faults in a cooling system based on acoustic emission signals (both in temporal and frequency domains) and/or imaging signals. Upon training the model to detect or predict system faults in a cooling system, acoustic emission signals may be obtained non-intrusively from the cooling system using acoustic emission sensors, hydrophones and/or microphones. Additionally, upon training the model to detect or predict system faults in a cooling system, imaging signals (e.g., boiling images) may be obtained non-intrusively from the cooling system using optical sensors (e.g., high-speed camera). The trained model may then detect or predict a system fault in the cooling system based on such information (acoustic emission signals, including in temporal and frequency domains, and/or the imaging signals).