Residual useful life prediction of rolling bearings based on improved Informer modeling

Accurate prediction of rolling bearings’ Remaining Useful Life (RUL) is critical for ensuring machinery reliability and safety. While deep learning offers considerable potential, prevailing prognostics models face significant challenges: they often overlook critical inter-sensor correlations, exhibit instability in long-term predictions, and demand extensive training data. These limitations severely hinder their efficacy in data-scarce or informationally redundant scenarios. To overcome these issues, this paper introduces a novel hybrid architecture that synergistically integrates Convolutional Neural Networks (CNNs) with the Informer model. The proposed framework is engineered to autonomously extract and fuse salient nonlinear spatiotemporal features from multi-sensor data streams. Raw sensor signals are first segmented via a sliding window approach to preserve degradation characteristics. Subsequently, stacked convolutional layers hierarchically learn high-level representations, effectively capturing both intra- and inter-sensor dependencies. These enriched features are then processed by the Informer module for efficient time-series encoding and long-term dependency modeling, ultimately yielding a precise RUL estimate through a fully-connected layer. Extensive experimental results on rolling-element bearing datasets demonstrate the superiority of our method. It achieves state-of-the-art prediction accuracy and markedly superior stability over time, even when trained with significantly reduced dataset sizes, confirming its robustness and practical utility.