基于改进LMD和MED的滚动轴承故障诊断研究

针对轨边声学轴承信号有用特征微弱、易被强噪声掩盖的问题,设计实现了一种将最小熵解卷积与改进局域均值分解相结合的方法,达到信号降噪与故障诊断目的。利用三次Hermite插值改善LMD并提高LMD分解精度。将采集到的强噪信号进行MED降噪,再利用改进LMD算法进行分解,使多分量信号分解成单分量信号,并计算各分量的峭度值,挑选出峭度值最大的分量,最后利用包络谱分析,提取滚动轴承的故障特征。计算信号的峰值信噪比(PSNR,Peak Signal to Noise Ratio),将其作为降噪指标,体现方法的降噪性能。实验结果表明,设计的方法应用于轴承故障诊断,能将信号信噪比提高5.13 dB,能精准定位并提取轴承缺陷位置和信号特征,具有较好降噪和信息分辨能力。

Fault diagnosis of rolling bearing based on improved LMD and MED

Aiming at the problem that the useful characteristics of the trackside acoustic bearing signal are weak and easy to be covered by strong noise, a method combining the minimum entropy deconvolution with the improved local mean decomposition is designed and implemented to achieve the purpose of signal noise reduction and fault diagnosis. The cubic Hermite interpolation was used to improve LMD and LMD decomposition accuracy.The collected strong noise signal was used for MED noise reduction, and then the improved LMD algorithm was used to decompose the multi-component signal into a single component signal. The kurtosis value of each component was calculated, and the component with the largest kurtosis value was selected. Finally, the envelope spectrum analysis was used to extract the fault characteristics of rolling bearing.The peak signal to noise ratio of the signal was calculated as the noise reduction index to reflect the noise reduction performance of the method.The experimental results show that the designed method can improve the signal-to-noise ratio by 5.13 dB, accurately locate and extract the location and signal characteristics of bearing defects, and has better noise reduction and information resolution ability.