噪声与振动控制2019，Vol.39Issue(1)：172-176,5.DOI:10.3969/j.issn.1006-1355.2019.01.033
基于LMS和Fast-Kurtogram的滚动轴承早期故障诊断
Early Fault Diagnosis of Rolling Bearings based on LMS and Fast-Kurtogram
摘要
Abstract
Due to the difficulty of early fault features extraction of rolling bearings, a new fault diagnosis method for rolling bearings based on LMS algorithm noise reduction, Fast-Kurtogram frequency selection and resonance demodulation technology is proposed. First of all, the adaptive noise reduction is used to reduce the effect of the background noise. Then, based on the characteristics of spectral kurtosis, which is sensitive to the transient components of the faulty signal, the optimal band center and bandwidth of filter can be determined by plotting the Fast-Kurtogram of the denoised signal. Finally, the resonance envelope demodulation is used to extract the early fault characteristics of the rolling bearing. The feasibility and efficiency of this proposed method for the early fault diagnosis of rolling bearing have been verified by simulation and experiments.关键词
振动与波/滚动轴承/故障诊断/Least Mean Square (LMS)/Fast-Kurtogram/共振解调Key words
vibration and wave/rolling bearing/fault diagnosis/Least Mean Square (LMS)/Fast-Kurtogram/resonance demodulation分类
机械制造引用本文复制引用
杨晓雨,荆双喜,罗志鹏..基于LMS和Fast-Kurtogram的滚动轴承早期故障诊断[J].噪声与振动控制,2019,39(1):172-176,5.基金项目
国家自然基金资助项目(U1604140,51775174) （U1604140,51775174）
河南省科技攻关资助项目(172102210021) （172102210021）
