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| 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 |
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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.
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2015, Vol. 36 
