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...

Full description

Saved in:

Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 24; no. 15; p. 4855
Main Authors:	Lee, Daehee, Choo, Hyunseung, Jeong, Jongpil
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.08.2024
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!