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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: 7/10/2026
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Updated: 9/13/2022
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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
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