基于多分支特征融合注意力的轴承故障诊断方法

doi:10.3969/j.issn.1000-1158.2025.02.10

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Abstract A multi-branch multi-scale convolutional neural network with channel attention （MBSACNN） method is proposed to enhance feature extraction and improve accuracy with noises in bearing fault diagnosis.Different from the traditional methods， where only one dimension fault feature was considered， multi-channel multi-input is constructed to extract multi-dimension abundant features and enhance the diversity of sample information from the wavelet transform time-frequency signal， which are combined by the channel attention mechanism.Higher diagnosis accuracy and better noise-robustness are obtained by the MBSACNN compared with the traditional methods， which is verified by Case Western Reserve University （CWRU） bearing dataset and real-world application in cement industry.In the case of a bearing dataset， the accuracy of zero noise and strong noise is 99.99% and 96.97% respectively.Under strong noise，the accuracy of three kinds of cement equipment are all above 97.25%.

Key words： vibration measurement bearing fault diagnosis convolutional neural networks multi-branch channel attention mechanism cement equipment

Received: 28 May 2024 Published: 04 March 2025

PACS:	TB936
	TH133.3

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	Articles by authors
	GUO Haiyu
	YU Wei
	ZHANG Xiaoguang
	LU Fanfan
	CHEN Yang
	ZHAO Xueyi

Cite this article:

GUO Haiyu,YU Wei,ZHANG Xiaoguang, et al. Bearing Fault Diagnosis Method Based on Multi-branch Feature Fusion Attention Mechanism[J]. Acta Metrologica Sinica, 2025, 46(2): 222-232.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2025.02.10 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2025/V46/I2/222

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