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

Daehee Lee1, Hyunseung Choo1, Jongpil Jeong2

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, Republic of Korea.

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Summary

This study introduces an advanced bearing failure diagnosis model. The graph convolution network (GCN)-based LSTM autoencoder with self-attention achieves high accuracy in identifying bearing defects in manufacturing.

Keywords:
bearing fault diagnosisfault simulatorgraph convolution network (GCN)long short-term memory (LSTM) autoencoderself-attention

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Industrial Automation

Background:

  • Manufacturing processes rely on efficient and reliable operation, with motor-related failures, particularly bearing defects, posing significant challenges.
  • Bearings are critical components for smooth mechanical movement, but their failure diagnosis is complicated by imbalanced data and complex time-series characteristics.
  • Existing artificial intelligence (AI) models like CNNs, LSTMs, SVMs, and XGBoost have shown limitations in accurately diagnosing bearing failures.

Purpose of the Study:

  • To develop a novel and highly accurate bearing failure diagnosis model.
  • To overcome the limitations of conventional AI models in handling imbalanced and complex time-series data for bearing fault detection.
  • To enhance the reliability and efficiency of manufacturing processes through improved fault diagnosis.

Main Methods:

  • A novel bearing failure diagnosis model was proposed, integrating a graph convolution network (GCN) with a long short-term memory (LSTM) autoencoder.
  • The model incorporates a self-attention mechanism to improve the analysis of complex time-series data.
  • Training and validation were performed using data from the Case Western Reserve University (CWRU) dataset and a dedicated fault simulator testbed.

Main Results:

  • The proposed GCN-based LSTM autoencoder with self-attention achieved a diagnostic accuracy of 97.3% on the CWRU dataset.
  • The model demonstrated exceptional performance on the fault simulator dataset, reaching an accuracy of 99.9%.
  • These results indicate superior performance compared to conventional AI models in bearing failure diagnosis.

Conclusions:

  • The developed GCN-based LSTM autoencoder with self-attention is a highly effective tool for bearing failure diagnosis in manufacturing.
  • This model addresses key challenges of data imbalance and time-series complexity, offering improved reliability for industrial equipment.
  • The findings suggest a promising direction for enhancing predictive maintenance and operational efficiency in the manufacturing sector.