Lightweight CNN Architecture Design Based on Spatial-Temporal Tensor and Its Application in Bearing Fault Diagnosis

Bearing is a failure-prone component in rotating machinery equipment, and various task conditions make bearing fault diagnosis (BFD) a challenging task. Despite the great success of convolutional neural network (CNN) in feature extraction, most of them are based on manual tuning, and it is not easy...

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Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 12
Hlavní autoři:	Wang, Zan, Lu, Hui, Shi, Yuhui, Wang, Xianpeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Automatic architecture design bearing fault diagnosis (BFD) convolutional neural network (CNN) Convolutional neural networks Deep learning Fault diagnosis Feature extraction Floating point arithmetic genetic algorithm Genetic algorithms Lightweight Machine learning Mathematical analysis Rotating machinery Space exploration Tensors Vibrations
ISSN:	0018-9456, 1557-9662
On-line přístup:	Získat plný text
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