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Hybrid Rotating Machinery Fault Diagnosis and Prognosis

Reference #: 01570 The University of South Carolina is offering licensing opportunities for Hybrid Rotating Machinery Fault Diagnosis and Prognosis Background: Bearing faults are the top contributor to the failure of rotating machinery systems. In wind energy systems, about 80% of gearbox failures are caused by bearing faults. According to verified...

Published: 8/22/2024 | Inventor(s): Guangxing Niu, Bin Zhang

Keywords(s): Continuous wavelet transform, convolutional neural network, Fault model selection, Particle filter, Rotating machinery systems, STP estimation

Category(s): Engineering and Physical Sciences