Bearing Fault Diagnosis Method Based on Cascaded Stochastic Resonance and FSResNet Network
编号:63 访问权限:仅限参会人 更新:2025-11-10 11:32:31 浏览:35次 口头报告

报告开始:2025年11月23日 09:50(Asia/Shanghai)

报告时间:20min

所在会场:[S2] Parallel Session 2 [S2-2] Parallel Session 2-23 AM

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摘要
As a key component of rotating machinery, rolling bearings play a critical role in supporting loads, reducing friction, and enabling power transmission. However, the harsh operating environment and feature aliasing among different faults make accurate classification of fault states challenging. In this paper, a rolling bearing fault classification method based on stochastic resonance and an enhanced residual network is proposed. First, a two-stage cascaded stochastic resonance model is constructed to suppress high-frequency noise and enhance weak impulse components. Second, a novel feature—Root mean square-Kurtosis Energy—is proposed, which integrates signal energy distribution and impact intensity through a nonlinear interaction mechanism, thereby improving separability between fault modes. Finally, an attention mechanism is incorporated into the skip connection module of the ResNet-V2 network, enabling spatial mapping and local enhancement of features to strengthen the network's response to key fault information and improve discriminative capability. Laboratory results demonstrate that the proposed method achieves high fault recognition accuracy under complex working conditions and can effectively distinguish fault categories.
 
关键词
Rolling bearings,stochastic resonance,root mean square-kurtosis energy,FSResNet,fault classification
报告人
Xiyu Pan
postgraduate China Jiliang University

稿件作者
Xiyu Pan China Jiliang University
Zhou Shao China Jiliang University
Yiding Wu China Jiliang University
Zuozhou Pan China Jiliang University
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重要日期
  • 会议日期

    11月21日

    2025

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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