An Improved Variational Autoencoder and Graph Attention Network Method for Wear Prediction of Aerospace Self-Lubricating Bearing Using Acoustic Emission Signal

Aerospace self-lubricating bearings are critical components in aircraft transmission systems, where wear-induced degradation under high-load and dynamic conditions poses significant challenges to operational safety and system longevity. In recent years, deep learning methods have shown promise in wear prediction by leveraging abundant monitoring data from sensor networks. However, these methods often struggle to detect early-stage degradation and rely on labor-intensive feature engineering, limiting their effectiveness in handling noisy, high-dimensional data. To overcome these issues, this article proposes an improved variational autoencoder and graph attention network method for wear prediction based on…

• NN
  National Natural Science Foundation of China

  Awards: 52275499, 92467101