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国外文献 > 1.轴承 > 5. 监测与诊断 > 状态监测

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标 题： A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition Methods: Application to bearing defect diagnosis
经验小波变换与经验模分解方法的比较研究：应用于轴承缺陷诊断
作 者： Kedadouche, M.
Thomas, M.
Tahan, A.
关键词： Bearing fault  [轴承故障]
Acoustic emission  [ 声发射]
Empirical Mode Decomposition (EMD)  [ 经验模态分解（EMD）]
Ensemble Empirical Mode Decomposition (EEMD)  [ 联合经验模态分解（EEMD）]
Empirical Wavelet Transform (EWT)  [ 经验小波变换（EWT）]
出版年： 2016
期刊名： Mechanical Systems & Signal Processing
机械系统与信号处理
年卷期： 2016, vol.81
摘 要： The Ensemble Empirical Mode Decomposition (EEMD) is a noise assisted method that may sometimes provide a significant improvement on Empirical Mode Decomposition (EMD). However, the amplitude and number of added noise need to be selected when applying the EEMD method. Furthermore, the computation time which depends on the number of ensemble trails is very high compared to the EMD. In this paper, a new way for choosing the appropriate added noise is presented. Conversely, a recently-developed method called the Empirical Wavelet Transform (EWT) is investigated. A comparative study between the EMD and EWT methods is conducted. The results show that the EWT is better than the EEMD and EMD on mode estimates and computation time is significantly reduced. An experimental study on bearing diagnosis is conducted. The EWT is applied to experimental data coming from damaged bearings. In the paper, an index selection is introduced that allows for the automatic selection of the Intrinsic Mode Functions (IMF) that should be used to perform the envelope spectrum. It is shown that choosing all the IMF selected by the index is more efficient than only choosing the best one. The envelope of the sum of the selected IMF clearly reveals the bearing frequencies and its harmonics which are excited by the defect. This approach seems to be an effective and efficient method for processing bearing fault signals. (C) 2016 Elsevier Ltd. All rights reserved.

集合经验模分解（EEMD）是一种借助噪声辅助的方法，有时能显著改善经验模分解（EMD）。然而，在应用EEMD方法时，必须选择添加的噪声的幅度和数量。此外，与EMD相比，依赖于集合迭代次数的计算时间非常高。本文提出了一种新的选择合适添加噪声的方法，并对最近发展起来的经验小波变换（EWT）方法进行了研究。同时，对EMD与EWT方法进行了对比分析。结果表明，EWT在模态估计方面优于EEMD和EMD，且计算时间显著减少。本文还开展了一项轴承诊断的实验研究，将EWT应用于来自受损轴承的实验数据。文中引入了一个索引选择方法，可自动选择用于构建包络谱的本征模函数（IMF），并证明采用该索引选出的所有IMF比仅选择最优一个更为高效。所选IMF的和的包络线清晰地揭示了由缺陷激发的轴承频率及其谐波。这种方法似乎是一种有效且高效的轴承故障信号处理手段。（C）2016 Elsevier Ltd. 保留所有权利。
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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