基于1.5维Teager能量谱的滚动轴承故障诊断

Rolling Bearing Fault Diagnosis Based on 1.5-dimensional Teager Energy Spectrum

针对强背景噪声下滚动轴承微弱故障信号特征难以提取的特点,提出了基于最小熵解卷积与1.5维Teager能量谱的滚动轴承故障诊断方法,首先利用最小熵解卷积(Minimum entropy deconvolution,MED)对强噪声下滚动轴承信号进行降噪处理,然后对降噪后的信号进行Teager能量算子解调,最后对解调后的信号进行1.5维谱分析。通过对内外圈故障的仿真信号及实验数据的处理分析,且与包络谱方法进行了对比,验证了该方法的有效性和准确性。

Based on the characteristics that it is difficult to extract weak fault signals of rolling bearings against the strong noise background, this paper proposes a new method of rolling bearing fault diagnosis based on minimum entropy deeonvolution and 1.5-dimensional Teager energy spectrum. Firstly, minimum entropy deconvolution （MED） is used to reduce noise of rolling bearings against the strong noise background, then Teager energy operator demodulation is conducted, and finally 1.5-dimensional spectrum analysis of demodulated signals is conducted. The effectiveness and accuracy of the proposed method has been validated through analysis of simulation signals of inner and outer ring faults and processing of experimental data, and comparison and contrast with envelope spectrum.