Planetary Gearbox Fault Diagnosis based on LMD Sample Entropy and ELM - Zhang Ning; Wei Xiuye; Xu Jinhong (Jixie chuandong)
In order to solve the difficult problem of early fault feature extraction of planetary gearbox and consider that the planetary gearbox vibration signal is coupling and nonlinear，and the signal has multiple transmission paths，a planetary gearbox fault diagnosis method based on Local Mean Decomposition（LMD） and Sample Entropy and Extreme Learning Machine（ELM） is proposed.Firstly，the vibration signal is adaptively decomposed into a plurality of PF components by LMD，and the first four PF components including the main fault information are selected in combination with the correlation coefficient and the variance contribution rate.Secondly，the Sample Entropy of the signal is calculated to form a feature vector.Finally，the feature vector is input into ELM for fault classification.Experiments are carried out on the planetary gearbox test bench，compared with the probabilistic neural network classification algorithm，and compared with the feature vector based on Singular Value Decomposition （SVD）.The results verify the effectiveness of the proposed method.