AI Research
Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction
Advancing predictive maintenance through frequency-domain graph modeling for industrial longevity.
Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spatial relationships by representing time series data as a sequence of graphs, have shown exceptional ...
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ArXiv CS.LG
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ArXiv CS.LG
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Integrating Fourier transforms into graph neural networks allows models to capture complex frequency-domain patterns essential for predicting industrial equipment failure. This approach significantly enhances the precision of remaining useful life estimations by better accounting for nonlinear degradation trends.
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