Work

Dynamic Energy Sparse Self-Attention Based on Informer for Remaining Useful Life of Rolling Bearings

Year: 2025

Type: article

Abstract: Predicting the remaining useful life of rolling bearings presents significant challenges due to their nonstationary degradation patterns. Conventional methods struggle with long sequences and are strongly dependent on manual feature extraction, which can lead to inefficiencies and biased predictions. To address these issues, this paper proposes the dynamic energy sparse self-attention model based on Informer. The proposed model uses the auto-reassignment transform to extract more accurate features from the raw vibration signals, and the dynamic energy sparse self-attention mechanism adapts the attention weights based on the energy distribution. In addition, the Monte Carlo dropout method is employed to quantify uncertainty and enhance model robustness. Experimental results obtained on two benchmark datasets demonstrate that the proposed model outperforms existing advanced methods, achieving a reduction in mean absolute error and root mean square error, demonstrating its superior ability to handle complex bearing degradation prediction tasks.

Source: IEEE Access

DOI: 10.1109/access.2025.3594077

Authors: Cheng Shi, Qifei Li, Hui Chen, Yuanwei Song, Qianqi Le

Institution: Chengdu University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075