摘要
针对不同转速下,不同损伤程度的滚动轴承内、外圈故障,提出一种基于局域均值分解(Local Mean De-composition,LMD)和Lempel-Ziv指标的滚动轴承损伤程度识别方法。LMD方法是一种新的自适应时频分析方法,将轴承振动信号分解为若干个瞬时频率有物理意义的乘积函数(Production Function,PF),再结合峭度条件找出蕴含故障信息的最优PF分量,计算其PF函数和包络的Lempel-Ziv的归一化值,再加权求和得到最终的Lempel-Ziv综合指标,表征了不同故障的损伤程度。同时还研究了在不同转速下的内、外圈故障轴承的Lempel-Ziv指标的分布规律,使结论更具有普遍性。经实验结果验证,此方法能有效地应用于滚动轴承的故障程度的诊断。
For different rotation speeds and different inner or outer race defects severity, a fauh severity assessment scheme based on local mean decomposition （LMD） and Lempel-Ziv index was put forward. In LMD method the bearing vibration signal was decomposed into several product functions （PF） with instantaneous frequency having definite physical meaning. The optimal PF component can be found according to kurtosis conditions. On the basis of the best PF, the normalized Lempel-Ziv values for the PF envelope were calculated. Then the values multiplied by given weights were summed up to form a final measure named the integrated Lempel-Ziv index. Different intervals of the index value correspond to different fault severity. At the same time, for making conclusions more universal, the bearing fault Lempel-Ziv index distribution rule of the inner and outer defects at different rotating speed was studied. The experiment results show that the algorithm can be effectively applied in rolling bearing fault diagnosis.
出处
《振动与冲击》
EI
CSCD
北大核心
2012年第16期77-82,共6页
Journal of Vibration and Shock
基金
内蒙古自治区高等学校科学研究项目(NJZY11148)资助
