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国外文献 > 1.轴承 > 5. 监测与诊断 > 故障诊断

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标 题： A Bearing Fault Detection Method Base on Compressed Sensing
基于压缩感知的轴承故障检测方法
作 者： Zhang Xinpeng
Hu Niaoqing
Cheng Zhe
关键词： Bearing fault detection  [轴承故障检测]
Nyquist sampling theorem  [ 奈奎斯特采样定理]
Compressed sampling  [ 压缩采样]
Matching pursuit  [ 匹配追踪]
出版年： 2015
文集名： Engineering Asset Management - Systems, Professional Practices and Certification
工程资产管理——系统、专业实践与认证
会议名： 8th World Congress on Engineering Asset Management (WCEAM 2013) & the 3rd International Conference on Utility Management & Safety (ICUMAS)
第八届世界工程资产管理大会（WCEAM 2013）暨第三届国际公用事业管理与安全会议（ICUMAS）
届 次： 8th
摘 要： For bearing fault detection in frequency domain, traditional methods estimate bearing fault condition based on mass data sampled by Nyquist sampling theorem, which will burden the storage. A new bearing fault detection method based on compressed sensing will be proposed in this paper in allusion to the problem mentioned above. The method presented here carried out compressive sampling and get a small set of incoherent projections, often the number of projections can be much smaller than the number of Nyquist rate samples. Then based on matching pursuit, the bearing condition will be estimated finally using these few measurements directly without ever reconstructing the signals involved. Sparsity of original signal is not demanded since the signal does not need to be recovered completely, which will also helped to expanded the method to other signals with similar characteristics in frequency domain. Related test will be achieved to verify the effectiveness of the method proposed in this paper.

为在频域中实现轴承故障检测，传统方法基于奈奎斯特采样定理采集的连续数据来估计轴承故障状态，这会带来较大的存储负担。本文针对上述问题提出了一种基于压缩感知的新轴承故障检测方法。该方法通过压缩采样获得一组非相干投影，通常投影数量远小于奈奎斯特采样率下的样本数。随后利用匹配追踪技术，直接使用这些少量测量值对轴承状态进行估计，无需重建原始信号。由于无需完全恢复原始信号，因此对信号稀疏性没有严格要求，这也使得该方法可扩展至其他具有相似频域特性的信号。文中还将进行相关实验验证，以证明所提方法的有效性。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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