Bearing Remaining Useful Life Prediction via Multi-Scale Convolution and Bidirectional Gated Recurrent Unit Network

Accurate remaining useful life RUL prediction of rolling bearings plays a vital role in industrial predictive maintenance. Nevertheless, current approaches fail to effectively extract multi-scale degradation features in noisy environments, resulting in significant prediction inaccuracies. We propose a Multi-Scale Convolutional Bidirectional Gated Recurrent Unit MSCNN-BiGRU network for bearing remaining useful life prediction. First, raw vibration signals undergo deep feature extraction via a Stacked Denoising Autoencoder SDAE , followed by dimensionality reduction using a Hierarchical Self-Organizing Map HSOM to generate a 1D degradation curve DC . A Multi-Scale Convolution module is then constructed, incorporating 1D dilated convolution and a multi-scale strategy to extract degradation features from the DC, enabling the simultaneous capture of localized defects and global trend patterns. Finally, an attention layer is integrated at the feature input stage, combined with a GRU to construct a Bidirectional GRU BiGRU prediction model, which dynamically weights critical temporal dependencies for accurate RUL estimation. Experiments on the PHM2012 dataset that MAE is reduced by an average of 18.7 compared to sub-optimal models, and this work provides a generalizable framework for RUL prediction of rotating machinery, enhancing the reliability of industrial maintenance systems.