Jia Feng, Lei Yaguo, Shan Hongkai, Lin Jing
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, China.
Sensors (Basel). 2015 Nov 20;15(11):29363-77. doi: 10.3390/s151129363.
The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.
滚动轴承早期故障特征在振动信号中十分微弱，因为这些信号通常被强烈的背景噪声所掩盖。为了从信号中提取轴承微弱的故障特征，基于最大相关峭度解卷积（MCKD）提出了一种改进的谱峭度（SK）方法。该方法结合了MCKD指示周期性故障瞬变的能力和SK在频域中定位这些瞬变的能力。利用一个被强噪声淹没的仿真信号来验证所提方法的有效性。结果表明，MCKD有助于清晰显示轴承信号的周期性脉冲分量，该方法能够检测信号的共振频带并提取其故障特征频率。通过分析从风力涡轮机和热轧机收集的实际振动信号，我们证实使用所提方法能够提取滚动轴承的故障特征并诊断早期故障。与SK方法对比验证了所提方法更适合诊断滚动轴承的早期故障。
