TE-FEDformer: A Time-Series-Enhanced FEDformer for Remaining Useful Life Prediction of Rolling Bearings

In the era of intelligence, accurate remaining useful life (RUL) prediction is essential to ensure the reliable operation of smart equipment, particularly for rolling bearings—critical components that are highly susceptible to degradation in rotating machinery. However, as faults progressively develop, the vibration signals of rolling bearings exhibit strong non-stationarity and complex degradation patterns. Existing RUL prediction methods, particularly standard Transformer-based models, often struggle to capture local transient features within non-stationary signals and fail to effectively decouple long-term degradation trends from periodic variations. To overcome these limitations, a novel RUL prediction method that integrates time-series analysis techniques with the FEDformer architecture is proposed, termed TE-FEDformer. Firstly, a feature enhancement module is employed at the input stage to reconstruct and strengthen the original sequence, aiming to strengthen the representation of weak fault features that are often overlooked by global attention mechanisms. Then, deep time-series representations are extracted via the encoder. In the decoding stage, a frequency enhancement mechanism and a sequence decomposition mechanism are jointly utilized to explicitly model the coupling between degradation trends and periodic variations, thus resolving the spectral interference commonly encountered in complex degradation processes. Comparative experimental results on the PHM2012 and XJTU-SY datasets demonstrate that TE-FEDformer outperforms other benchmark models. Ablation studies further validate that each module contributes positively to the overall performance, confirming the effectiveness of the proposed approach for RUL prediction.