| 标题 |
 Small sample cross-domain bearing fault diagnosis method based on signal denoising lightweight model 相关领域
方位（导航）
信号（编程语言）
断层（地质）
计算机科学
降噪
领域（数学分析）
样品（材料）
模式识别（心理学）
人工智能
地质学
数学
地震学
色谱法
化学
数学分析
程序设计语言
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| 网址 | |
| DOI | |
| 其它 |
Abstract During the operation of bearings, their structural parameters, loads, signal transmission paths, etc, are highly dynamic, resulting in obvious distribution differences in the statistical characteristics of the data. In addition, the real fault data set is often too small and lacks fault labels, resulting in poor performance of data-driven neural networks in small sample cross-domain fault diagnosis. In order to solve the above problems faced by engineering practice and achieve high-precision fault diagnosis, a signal denoising lightweight (FMECR-18) model for small sample cross-domain bearing fault diagnosis is proposed. First, the two-domain signal data is denoised using a denoising module (FME) based on the combination of eigenmode decomposition (FMD) and multiscale entropy screening (MSE), and the denoised vibration signal is upgraded to a multi-dimensional signal through channel expansion. |
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