Journal Article

Bearing fault detection by using graph autoencoder and ensemble learning

Wang, Meng, Leng, Hongyong, Yu, Jiong, Du, Xusheng, Liu, Yiran

2024

Overview

The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166/987 / 639/166/988 / Algorithms / Artificial intelligence / Automation / Bearing fault detection / Bearings / Ensemble learning / Fault diagnosis / Graph neural network / Humanities and Social Sciences / Information processing / Intelligent fault detection / Learning / Life span / Machine learning / Machinery / Methods / multidisciplinary / Neural networks / Optimization / Outlier detection / Science / Science (multidisciplinary) / Signal processing / Spectrum analysis / Stochasticity / Vibration analysis / Wavelet transforms