报告详情
Application of Deconvolution Method for Extracting Sub-Gaussian Components Based on Minimize Residual Strategy under Low SNR
编号：57
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更新：2025-06-20 16:18:47 浏览：35次
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摘要
Vibration signals from machinery are widely utilized for equipment fault diagnosis. The acquired vibration signals often carry a significant amount of noise, resulting in a low signal-to-noise ratio (SNR), which poses challenges to the diagnostic process. Existing diagnostic methods generally perform well in diagnosing faults from vibration signals with high SNR, but they often exhibit poor performance under low SNR conditions. In this paper, a fault diagnosis method based on the Minimize Residual strategy for extracting sub-Gaussian-like distribution components is proposed (MRSE). This method leverages the characteristic of fault signals tending to approximate Gaussian distributions at low SNRs. By using variance as the objective function and minimizing residuals through Lanczos decomposition for iterative filtering, the variance of the filtered signal is reduced to extract components in the signal that approximate Gaussian distributions, thereby obtaining the filtered signal containing the fault information. Simulation experiments demonstrate that MRSE outperforms traditional methods across all metrics. In practical experiments, MRSE has been shown to more effectively extract fault features from bearings and gearboxes compared to traditional methods.
关键词
Fault diagnosis, Minimize residual, sub-Gaussian distribution, low SNR, Lanczos decomposition
报告人
Qijin Lin
student Tsinghua University稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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