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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: 5/16/2023
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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
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
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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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