公开(公告)号：US20240288130A1

公开(公告)日：2024-08-29

申请号：US18585161

申请日：2024-02-23

申请人： The Hong Kong University of Science and Technology

发明人： Moez LOUATI ,  Muhammad WAQAR ,  Mohamed Houcem Eddine BEN ARAB

IPC分类号： F17D5/02 ,  G01M3/28

CPC分类号： F17D5/02 ,  G01M3/2815

摘要： A technique for detection, classification and location identification of defects in a pressurized pipe network uses supervised machine learning and transient response signals (TRSs) of potential defect scenarios as training data. The TRSs are obtained from a model and/or measurements. The TRSs are arranged in an appropriate matrix form and its singular vectors (SVs) are obtained using singular value decomposition. The defect detection and classification procedure is conducted by projecting a measured TRS of the pipe network into a SV space. The location of the measured TRS in the SV space indicates the state of defective pipe section (if any), classify the defect according to clusters of training data (e.g., leak, blockage), and locate the defect within an identified defective section. The technique can accurately detect defective pipes in a network with a training data set as small as three scenarios per pipe section and one single measurement location.