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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: 7/10/2026
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Updated: 11/15/2022
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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
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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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DRCNN for Multi-task Bearing Fault Diagnosis with Information Fusion
Reference #: 01571
The University of South Carolina is offering licensing opportunities for DRCNN for Multi-task Bearing Fault Diagnosis with Information Fusion
Background:
First, most industrial systems are working in variable operating conditions and environments. The information of operating conditions, such as load profile, rotating speed, and...
Published: 7/10/2026
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Updated: 7/27/2022
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Inventor(s): Guangxing Niu, Bin Zhang
Keywords(s): Bearing, Deep residual convolutional neural network, Discriminate Feature Learning, Information Fusion, Multi-task Fault Diagnosis
Category(s): Engineering and Physical Sciences
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Lithium-ion battery health management based on single particle model
Reference #: 01494
The University of South Carolina is offering licensing opportunities for Lithium-ion battery health management based on single particle model
Background:
A single particle model is used in simulating the behavior of lithium-ion battery. Particle swarm optimization is used to identify the parameters of the single particle model....
Published: 7/10/2026
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Updated: 6/9/2022
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Inventor(s): Guangxing Niu, Bin Zhang
Keywords(s): Bayesian approach, Lebesgue sampling, Particle swarm optimization, Single particle model, State of charge, State of health
Category(s): Energy, Engineering and Physical Sciences
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