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 acoustic emission (AE) signals. Firstly, a clustering-guided contrastive variational autoencoder (CGC-VAE) model is proposed to process noisy, high-dimensional AE signals. The CGC-VAE employs K-means clustering to segment wear stages, combined with gaussian mixture model (GMM) regularization and contrastive learning, to extract low-dimensional, discriminative latent features. Subsequently, a temporal graph attention network (T-GAT) is proposed to construct a dynamic graph based on temporal proximity and feature similarity, which can effectively model the spatiotemporal relationships of latent features. It employs graph attention mechanism and LSTM layer for accurate wear prediction. Finally, experimental validation on aerospace self-lubricating bearing datasets, covering full lifecycle and partial wear scenarios, demonstrates the superior accuracy and adaptability of the proposed method.