Intelligent Fault Diagnosis of Rolling Bearings Based on Vmd-cnn-transformer
Year: 2025
Type: article
Abstract: The rapid development of deep learning has brought transformative advances to intelligent fault diagnosis, providing powerful end-to-end feature learning capabilities that enable more effective analysis of rolling bearing vibration signals. However, conventional convolutional neural network (CNN), with their fixed architectures, have difficulty capturing the dynamically changing time-frequency features of vibration signals. In addition, most existing models lack effective mechanisms to suppress noise and vibration interference during monitoring, leading to a marked drop in diagnostic accuracy under non-stationary and noisy conditions.To improve the model’s ability to process non-stationary signals, this study introduces a multi-module diagnostic framework, VMD-CNN-Transformer, which integrates Variational Mode Decomposition (VMD), CNN, and Transformer architectures. The framework first applies VMD to decompose the vibration signals into representative intrinsic mode functions, enhancing the multi-scale representation of the original signals. The CNN module then extracts key local features and integrates multi-scale information. Finally, the Transformer captures long-range dependencies, allowing detailed characterization of complex fault patterns.Comparative experiments on benchmark datasets, including CWRU, XJTU, and DIRG, show that the proposed method achieves superior robustness and generalization under challenging conditions with noise and varying operating states. The framework outperforms mainstream methods and provides a novel technical solution for intelligent industrial equipment monitoring, demonstrating strong potential for practical engineering applications.
DOI: 10.61784/wjer3029
Author: JinYuan Hu
Institution: Wuhan University of Science and Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411576444