Gear Fault Feature Extraction Applying MOMEDA and Mathematical Morphology Filtering
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摘要: 为了实现齿轮故障特征的有效提取，针对齿轮早期故障振动信号非线性、非平稳且信噪比低的特点，提出了一种基于多点最优最小熵反卷积（MOMEDA）和数学形态滤波的齿轮故障特征提取方法。首先利用MOMEDA恢复信号中的周期性故障特征并实现信号的降噪，再运用形态差值滤波器对解卷积后的信号进行滤波以增强信号中的冲击特征，最后对滤波结果求取频谱以进行故障特征提取；通过对仿真结果和实验数据的分析验证了该方法的可行性和有效性。结果表明，该方法具有抑制噪声和提取周期性故障冲击特征的能力，能够实现齿轮故障特征的提取。Abstract: In order to extract gear fault features effectively form early fault vibration signals, a new method of gear fault feature extraction based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and mathematical morphological filtering is proposed, aiming at the non-linear, non-stationary and low signal-to-noise ratio characteristics of gear early fault vibration signals. Firstly, the periodic fault features in the signal are recovered by MOMEDA and the noise of the signal is reduced. Then the morphological difference filter is used to filter the deconvolution signal to enhance the impact characteristics in the signal. Finally, the spectrum of the filtering results is obtained to extract the gear fault features. The simulation and experimental results show that the method has the ability to suppress noise and extract periodic impact features and can realize the extraction of gear fault features.
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Key words:
- MOMEDA /
- mathematical morphological filtering /
- gear fault /
- feature extraction
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