RESEARCH PAPER
Research on Intelligent Fault Diagnosis Method of Rotating Machinery under Noisy Environment Based on Graph Neural Network
1
The Army Engineering University of the PLA of China, China
Submission date: 2025-11-19
Final revision date: 2026-01-14
Acceptance date: 2026-03-11
Online publication date: 2026-03-23
KEYWORDS
graph neural networkmultichannel vibration signalenvelope activationcross-correlation functionstrong noise working condition
TOPICS
ABSTRACT
In response to the challenges of fault diagnosis for industrial equipment in high-noise environments, this study proposes an improved graph neural network model that combines envelope activation and cross-correlation function. This model first enhances single-channel feature extraction and anti-noise ability through an improved independent parallel one-dimensional convolution structure and envelope activation operation, achieving single-channel noise suppression and feature extraction; then, it uses the time-domain cross-correlation function to construct a topological graph structure between channels, highlighting the essential correlations of multi-channel signals, and realizes multi-channel feature fusion through graph convolution networks. Experimental results on the public bearing dataset show that proposed model has high classification accuracy, superior anti-noise performance, and good generalization ability in high-noise environments.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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