Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Advances in Mechanical Engineering

DOI: 10.1177/16878132261438560