Adaptive MAGNN-TCN: An Innovative Approach for Bearings Remaining Useful Life Prediction

With advancements in industrial automation, the accurate prediction of the remaining useful life (RUL) in bearings is crucial for the proactive maintenance and reliability of industrial machinery. Traditional machine learning approaches often rely heavily on manual feature engineering and struggle to capture complex, nonlinear interdependencies between features that are vital for understanding machinery behavior under varying operational conditions. Addressing these limitations, our research introduces an innovative deep learning framework that integrates multiadaptive graph neural networks (MAGNNs) with temporal convolutional networks (TCNs), thereby harnessing the power of graph-based learning to model complex interdependencies directly from raw sensor data. Our MAGNN framework employs a dynamic adjacency matrix that adapts to reflect the changing operational states of bearings, enabling the model to maintain high predictive accuracy even under fluctuating conditions. This adaptability is enhanced through a multiscale feature extraction strategy that captures temporal patterns across different resolutions, providing a comprehensive feature set that is robust against environmental noise and operational variability. Experimental validation on the PHM2012 and XJTU datasets indicates the advanced performance of our MAGNN framework, significantly outperforming established AI benchmarks such as GNN-TCN, GNN-GRU, and Transformer models. In particular, the MAGNN-TCN configuration achieves a substantial improvement, reducing RMSE by up to 36.04% and reducing MAE by up to 34.19% when compared to the best of these conventional graph-based and sequence models. This performance boost highlights the effectiveness of our approach in leveraging dynamic, adaptive graph structures and multiscale feature extraction, which are crucial for capturing the complex nonlinear interrelationships inherent in sensor data from mechanical systems.