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
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Beschreibung
Zusammenfassung: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 manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studied. However, the imbalance in failure data and the complexity of time series data make diagnosis challenging. Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM), and extreme gradient boosting (XGBoost)) face limitations in diagnosing such failures. To address this problem, this paper proposes a bearing failure diagnosis model using a graph convolution network (GCN)-based LSTM autoencoder with self-attention. The model was trained on data extracted from the Case Western Reserve University (CWRU) dataset and a fault simulator testbed. The proposed model achieved 97.3% accuracy on the CWRU dataset and 99.9% accuracy on the fault simulator dataset.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24154855