In environments with strong noise and varying loads, accurate fault diagnosis remains a significant challenge, especially in industrial applications where early fault detection is crucial for ensuring equipment reliability and operational safety. Traditional convolutional neural networks (CNNs) and graph convolutional networks (GCNs) often perform poorly under such complex conditions. This paper proposes a novel multibranch graph convolutional neural network (MBGCN) integrated with Gaussian filters to diagnose rotating machinery faults in noisy and variable load environments. The framework combines CNNs, Gaussian filter branches, and denoising autoencoders for efficient feature extraction and fusion. The enhanced multi-receptive field GCN captures information from different receptive fields optimizes node representations, and effectively mitigates noise interference. Comparative experimental results demonstrate that MBGCN outperforms traditional methods in fault diagnosis under complex industrial conditions, highlighting its potential real-world applications in predictive maintenance and intelligent monitoring systems.
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