RESEARCH PAPER
A Bearing Fault Diagnosis Method Using a Double Branch Lightweight Network Under Noise Interference
1
School of Electronic Information, Luoyang Institute of Science and Technology, China
2
School of Computer Science, Luoyang Institute of Science and Technology, China
3
Henan Key Laboratory of Green Building Materials Manufacturing and Intelligent Equipment, China
Submission date: 2025-09-16
Final revision date: 2025-12-19
Acceptance date: 2026-02-19
Online publication date: 2026-03-01
Corresponding author
Guoqiang Wang
School of Computer Science, Luoyang Institute of Science and Technology, 471023, Luoyang, China
School of Computer Science, Luoyang Institute of Science and Technology, 471023, Luoyang, China
KEYWORDS
TOPICS
ABSTRACT
For practical industrial production scenarios where collected vibration signals are easily interfered by environmental noise and bearing operating conditions are complex and variable, this paper proposes a Double Branch Lightweight Convolutional Neural Network (DBLCNN). The model adopts a dual-branch architecture: the one-dimensional branch enhances feature extraction capability under low signal-to-noise ratio conditions, while the two-dimensional branch improves feature representation while significantly reducing the number of parameters. The complementary fault features extracted by the dual branches effectively enhance the accuracy of fault diagnosis. Under varying operating conditions, the model achieves an average accuracy of 95.58%; with the addition of 0 dB Gaussian white noise, its average accuracy under varying conditions remains at 90.17%. This study demonstrates that, even based on raw vibration signals without cumbersome preprocessing, the model can achieve excellent diagnostic performance in noisy environments and under variable operating conditions.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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