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WLGD-Net: A Wavelet Local–Global Network for Denoising Rolling Bearing Vibration Signals
编号：94 访问权限：仅限参会人 更新：2026-09-24 21:45:25 浏览：1次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

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摘要
Fault-related transients in rolling bearing vibration signals are often masked by mixed disturbances, while clean reference measurements are rarely available. This paper proposes a Wavelet Local–Global Denoising Network (WLGD-Net), which combines fixed five-level Haar decomposition, local convolutional and self-attention branches, and residual subtraction. The network is trained using physically simulated mechanical signals with composite disturbances and requires no fault-frequency input during inference. A simulation with a known clean reference and a laboratory outer-race-fault record are used to evaluate waveform recovery, transient preservation, fault-harmonic retention, and residual leakage. Results show that WLGD-Net suppresses mixed disturbances while retaining recognizable fault transients and envelope-spectrum harmonics under the tested conditions.
关键词
vibration denoising; wavelet transform; residual learning; envelope spectrum; physical simulation
报告人
Yangkun Li
Mr. North China Electric Power University

稿件作者
Yangkun Li North China Electric Power University
Liqi Zhao North China Electric Power University
Wenwei Luo North China Electric Power University
Jiatie Li North China Electric Power University
Aijun Hu North China Electric Power University
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重要日期

  • 会议日期

    11月06日

    2026

    至

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
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