A renewed adversarial network for bearing fault diagnosis based on vibro-acoustic signals under speed fluctuating conditions
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Published:2023-11-16
Issue:
Volume:
Page:
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ISSN:1077-5463
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Container-title:Journal of Vibration and Control
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language:en
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Short-container-title:Journal of Vibration and Control
Author:
Xing Shuo1ORCID,
Wang Jinrui1,
Han Baokun1,
Zhang Zongzhen1,
Ma Hao1,
Jiang Xingwang1,
Ma Junqing1,
Yao Shunxiang1,
Yang Zujie1,
Bao Huaiqian1
Affiliation:
1. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China
Abstract
Large discrepancy of sample distribution resulting from speed fluctuation is a great challenge to mechanical equipment health monitoring. Existing fault diagnosis methods are often limited by the acquisition mechanism of single-modal measurement. Considering the above problems, a multidimensional features dynamically adjusted adaptive network (MFDAAN) fused vibro-acoustic modal signals is proposed in this paper. The MFDAAN considers the context information of activation features by Funnel activation (FReLU) function to activate the vibro-acoustic signal features. In order to obtain fusion features, the multidimensional features of vibro-acoustic signals are dynamically adjusted at different stages by channel attention mechanisms, which is capable of considering the global information. Wasserstein distance is employed in the domain-adversarial training strategy to improve the property extracting domain-invariant features. The effectiveness of the MFDAAN is verified by cross-domain fault diagnosis experiments in two different scenarios. The results show that the MFDAAN can achieve good diagnostic effect for the tasks set of cross-domain fault diagnosis.
Funder
Natural Science Foundation of Shandong Province
National Natural Science Foundation of China
Publisher
SAGE Publications
Subject
Mechanical Engineering,Mechanics of Materials,Aerospace Engineering,Automotive Engineering,General Materials Science
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