Tool wear recognition and signal labeling with small cross-labeled samples in impeller machining

Data-driven deep learning method is the main way to study the condition monitoring of mechanical equipment, in which sufficient labeled signals to train the model parameters is a typical problem. The existing methods to obtain the labeled signals mainly focus on manual marking. For the non-batch imp...

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Vydané v:	International journal of advanced manufacturing technology Ročník 123; číslo 11-12; s. 3845 - 3856
Hlavní autori:	Ou, Jiayu, Li, Hongkun, Wang, Zhaodong, Yang, Chao, Peng, Defeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.12.2022 Springer Nature B.V
Predmet:	Advanced manufacturing technologies CAE) and Design Classification Computer-Aided Engineering (CAD Condition monitoring Conditional probability Conditional random fields Deep learning Engineering Fault diagnosis Impellers Industrial and Production Engineering Labeling Labels Laboratories Machine tools Machining Machining centres Marking Mechanical Engineering Media Management Neural networks Original Article Signal processing Three dimensional imaging Time series Tool wear Small cross-labeled samples Deep conditional random field neural network Signal labeling Tool wear recognition
ISSN:	0268-3768, 1433-3015
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