Method of Rotating Machinery Fault Diagnosis Based on LMD and Local Time-frequency Entropy
MENG Zong1,2,LI Shan-shan1,2,WANG Ya-chao1,2
1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Heibei 066004, China;
2. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Qinhuangdao, Heibei 066004, China
Abstract：A method based on LMD (Local Mean Decomposition) and local time-frequency entropy in rotating machinery fault diagnosis is proposed. Aiming at bearing, the vibrating signal is decomposed into PFs (Product Functions) by LMD, and then Hilbert transformation is applied to every PF to get time-frequency distribution. The local time-frequency entropy is introduced to study the energy in time-frequency distribution quantitatively. In detail, according to the spectrum characteristic of bearing fault, the whole time-frequency plane is divided into some segments, and whose entropies are calculated to extract the fault feature of the bearing. By the method of fault feature extraction based on local time-frequency entropy, differences among the segments could be reflected in large. Also the computational complexity is reduced at the same time. The results of simulation and experiment are presented to verify the theory analysis.
［1］钟秉林,黄仁.机械故障诊断学［M］.3版.北京:机械工业出版社,2007.
［2］韩捷,张瑞林.旋转机械故障机理及诊断技术［M］.北京:机械工业出版社,1997.
［3］孟宗,顾海燕,刘利晖,等. 基于EMD与AR谱的轧机主传动系统故障诊断研究［J］.计量学报,2011, 32(4): 338-342.
［4］谢平,王欢,杜义浩. 基于EMD和Wigner-Ville分布的机械故障诊断方法研究［J］.计量学报,2010, 31(5): 390-394.
［5］汤宝平,董绍江,马靖华. 基于独立分量分析的EMD模态混叠消除方法研究［J］. 仪器仪表学报,2012,33(7):1477- 1482.
［6］杜陈艳,张偷锋,杨平,等.经验模态分解边缘效应抑制方法综述［J］. 仪器仪表学报,2009,30(1):55-60.
［7］Smith J S. The local mean decomposition and its application to EEG perception data［J］. Journal of the Royal Society Interface, 2005, 2(5):443-454.
［8］Yang Y, Cheng J S, Zhang K. An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems［J］. Measurement,2012,45（3）561-570.
［9］鞠萍华,秦树人,赵玲.基于LMD的能量算子解调方法及其在故障特征信号提取中的应用［J］. 振动与冲击, 2011,30(2):1-4.
［10］程军圣,张亢,杨宇.局部均值分解方法及其在滚动轴承故障诊断中的应用［J］.中国机械工程,2009,20(22):2711- 2717.
［11］孟宗,王亚超,王晓燕.基于局部均值分解和极值延拓的旋转机械故障特征提取方法［J］.计量学报,2014, 35(5): 469-473.
［12］林京,屈梁生.信号时频熵及其在齿轮裂纹识别中的应用［J］.机械传动,1998,22(2):37-39.
［13］于德介,张嵬,程军圣,等.基于EMD的时频熵在齿轮故障诊断中的应用［J］.振动与冲击,2005,24(5):26-29.
2015, Vol. 36 
