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为了解决强背景噪声环境下直升机滚动轴承故障信号微弱,故障特征难以提取的问题,提出一种基于最小熵解卷积(Minimum Entropy Deconvolution,MED)与Teager能量算子(Teager Energy Operator,TEO)的滚动轴承故障特征提取的新方法。根据滚动轴承故障信号表现为冲击波形的特点和MED降噪对冲击特征敏感的特性,采用MED对故障信号进行降噪处理,同时增强信号中的冲击成分;再结合TEO适合检测信号的瞬时变化,能有效提取故障信号冲击特征的特点,计算降噪信号的Teager能量信号,进行频谱分析提取滚动轴承的故障特征。通过对仿真信号和直升机滚动轴承混合故障信号进行分析,实验结果表明,该方法能有效提取强背景噪声环境中的微弱复合故障特征,具有一定的工程应用价值。
In order to solve the problem that the fault signal of helicopter rolling bearing is weak and the fault feature is difficult to extract under strong background noise environment, a rolling bearing based on Minimum Entropy Deconvolution (MED) and Teager Energy Operator (TEO) A new method of fault feature extraction. According to the characteristics of the shock waveforms and the characteristics of the MED noise attenuation on the impact characteristics, according to the characteristics of the rolling bearing fault signal, the MED is used to reduce the noise of the fault signal and enhance the impact components in the signal. Combined with the TEO, Effectively extract the characteristics of the fault signal impact characteristics, calculate the Teager energy signal of the noise reduction signal, and conduct spectrum analysis to extract the fault characteristics of the rolling bearing. The simulation results show that the proposed method can effectively extract weak composite fault features in a strong background noise environment and has certain engineering application value.