GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturi...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 24; H. 15; S. 4855
Hauptverfasser:	Lee, Daehee, Choo, Hyunseung, Jeong, Jongpil
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 01.08.2024
Schlagworte:	Accuracy Acoustics Analysis Artificial intelligence bearing fault diagnosis Bearings Computational linguistics Datasets Deep learning Failure Fault diagnosis fault simulator Fourier transforms graph convolution network (GCN) Language processing long short-term memory (LSTM) autoencoder Maintenance costs Manufacturing Natural language interfaces Neural networks Preventive maintenance Productivity Research methodology self-attention Sensors Social networks Time series Vibration analysis Wavelet transforms fault simulator self-attention graph convolution network (GCN) long short-term memory (LSTM) autoencoder bearing fault diagnosis
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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