Fault Diagnosis of Rolling Bearing Using Convolutional Denoising Autoencoder and Siamese Neural Network With Small Sample

Bearing fault diagnosis is critical for ensuring mechanical reliability and operational safety. Industrial Internet of Things (IIoT) sensors provide real-time monitoring data, advancing research in data-driven approaches to bearing fault diagnosis. However, current studies overlook two key challenge...

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Veröffentlicht in:	IEEE internet of things journal Jg. 12; H. 5; S. 5233 - 5244
Hauptverfasser:	Zhao, Xufeng, Chen, Ying, Yang, Mengshu, Xiang, Jiawei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial neural networks Automation Classification Convolution Convolutional codes Convolutional denoising autoencoder (DAE) Data models Decoding Fault diagnosis Feature extraction Industrial applications Industrial Internet of Things Internet of Things Neural networks Noise Noise reduction Real time Roller bearings rolling bearing Rolling bearings siamese neural network signal processing small sample Training White noise
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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