Novel integrated health indicator and DRSN-TCN based remaining useful life prediction for rolling bearings

Abstract Accurately predicting the remaining useful life of bearings is crucial for ensuring the reliability and safety of rotating machinery across various industrial scenarios. For instance, the bearings of air compressors in fuel cells, as key components, directly affect the reliability of the entire system. However, existing prediction methods still face three core challenges: insufficient extraction of degradation features, low network prediction accuracy, and inadequate local information acquisition capability of prediction networks. To address these issues, this paper proposes a novel deep residual shrinkage network-temporal convolutional network (DRSN-TCN) prediction framework integrated with weighted health indicator (HI) and a multi-scale attention mechanism, with its core innovations reflected in three aspects: first, a weighted feature fusion method is proposed to construct nonlinear HIs, which evaluates the contribution of each statistical feature to the degradation process based on three quantitative metrics—monotonicity, trendability, and prognosability, strengthens key degradation information and suppresses noise interference, and ultimately generates HIs with richer degradation information. Second, a DRSN-TCN integrated architecture is designed, where embedding the DRSN module into the TCN network not only expands network depth to enhance deep feature extraction capability but also mitigates the gradient explosion problem via residual learning and soft thresholding mechanisms. Third, a multi-scale attention mechanism is innovatively introduced to enable the model to simultaneously focus on the short-term fluctuations and long-term evolutionary trends of bearing degradation, thereby alleviating the local information loss issue of TCN caused by oversized dilated convolution kernels. Validation results based on two bearing durability test datasets show that, compared with current mainstream state-of-the-art methods, the proposed framework reduces the root mean square error by at least 31% and the mean absolute error by at least 24%, fully verifying its superior prediction accuracy and robustness.