ArticleSensors (Basel, Switzerland)2024
GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Few-Shot Cross-Domain Fault Diagnosis via Wavelet Convolution Embedding and BDC-Based Metric Meta-Learning.Sensors (Basel, Switzerland) · 2026Article
- Edge Intelligence-Driven Bearing Fault Diagnosis: A Lightweight Anti-Noise Diagnostic Framework.Sensors (Basel, Switzerland) · 2026Article
- DrugBERT: a BERT-based approach integrating LDA topic embedding and efficacy-aware mechanism for predicting anti-tumor drug efficacy.Journal of translational medicine · 2025Article
- Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion.Sensors (Basel, Switzerland) · 2025Article
- Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions.Scientific reports · 2025Article
- Lightweight mechanical equipment fault diagnosis framework based on GCGAN-MDSCNN-ICA model.Scientific reports · 2025Article
- Spatio-temporal dynamic functional brain network for mild cognitive impairment analysis.Frontiers in neuroscience · 2025Article
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Authors and funding
3 authors.
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Abstract
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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Registered trials
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