Application of Improved Empirical Wavelet Transform in Gear Low Frequency Weak Fault Feature Extraction
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摘要: 齿轮振动信号的经验小波变换频谱分割, 可能会将啮合频率及其边频带划分到不同频带上, 导致频带划分不合理, 分离出的调幅-调频(AM-FM)分量不理想。针对上述主要问题, 提出了一种采用频谱趋势进行频谱边界划分的改进经验小波变换方法, 将齿轮啮合频率与其相应的边频带划分到同一频带内, 得到比较理想的AM-FM分量, 实现了依据齿轮振动信号频谱局部特征的自适应分解。同时, 对提取的AM-FM分量进行自相关分析以进一步增强改进经验小波变换的低频微弱故障特征提取效果。通过仿真与试验分析, 验证了提出方法在齿轮低频微弱故障特征提取中的有效性及优势。Abstract: In the spectrum boundary division of gear vibration siganl based on the empirical wavelet transform (EWT), the gear meshing frequency and its sidebands may be divided into different frequency bands, which leads to the unreasonable frequency band division and unsatisfactory AM-FM components. In view of the above problem, this paper proposes an improved empirical wavelet transform method to divide the spectrum boundary according to the spectrum trend. This method can divide the gear meshing frequency and its corresponding sideband into the same frequency band. Then, we can obtain an ideal AM-FM component and realize the adaptive decomposition according to the local characteristics of gear vibration signal spectrum. At the same time, auto-correlation analysis is performed on the extracted AM-FM component, which further enhances the low-frequency weak fault feature extraction effect of the improved EWT. Through the simulation and experiment, the effectiveness and advantage of this proposed method in the low-frequency weak fault feature extraction of the gear are verified.
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