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Lebesgue-Sampling-based Deep Learning for Battery Diagnosis and Prognosis

Reference #: 01603 The University of South Carolina is offering licensing opportunities for Lebesgue-Sampling-based Deep Learning for Battery Diagnosis and Prognosis Background: Accurate and efficient modeling of battery degradation is of great challenge and is becoming more and more complex for batteries in modern applications. Traditional degradation...

Published: 11/15/2022 | Inventor(s): Bin Zhang, Guangxing Niu

Keywords(s): Deep belief network, Diagnosis and prognosis, Fault dynamic model, Lebesgue sampling, Lithium-ion battery, Particle filter, Uncertainty management

Category(s): Engineering and Physical Sciences, Energy

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: 9/13/2022 | 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