An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault det...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 35; číslo 5; s. 6231 - 6242
Hlavní autori:	Zhang, Jiusi, Zhang, Ke, An, Yiyao, Luo, Hao, Yin, Shen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Classification Datasets Fault classification Fault detection Fault diagnosis Feature extraction imbalanced sample condition Machine learning Mechanical systems modified denoising autoencoder with self-attention mechanism for bottleneck layer (MDAE-SAMB) Multitasking Neurons Representation learning Representations Rotor dynamics Task analysis Training Transfer learning unknown fault identification
ISSN:	2162-237X, 2162-2388, 2162-2388
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