Remaining Useful Life Prediction of Rolling Bearings Based on Deep Time–Frequency Synergistic Memory Neural Network

Rolling bearings are essential components of a rotating machinery system. Surface imperfections on bearings can alter vibration patterns, and monitoring these changes allows for the precise prediction of the bearing’s remaining useful life (RUL). To address the limitations, such as inadequate sensitivity to features and constrained time–frequency feature extraction capabilities, in conventional methods for predicting the RUL of rolling bearings in the early stages of degradation, this paper introduces a novel predictive framework that combines dynamic weighting mechanisms with hybrid deep learning. This framework incorporates a continuous wavelet transform to generate two-dimensional time–frequency feature maps as degradation indicators, employs CNN for extracting local detailed features, integrates iTransformer modules with dynamic weighting mechanisms to enhance the focus on early subtle features, and leverages the time-dependent modeling capabilities of BiLSTM. The experimental findings using truncated samples from IEEE-PHM2012 datasets show a 71.82% reduction in errors compared with traditional CNN in the early prediction stages, where it effectively mitigated the challenge of early degradation features being overshadowed by noise. Ablation experiments on model components further validated the effectiveness of the model architecture design, where the dynamic weighting mechanism contributed significantly (29.92%) to improving the mean absolute error (MAE).