Application of EMPE and KP-KELM in Fault Diagnosis of Planetary Gearbox - Bo Qin; Heng Yin; Zhuo Wang; Jianqiang Zhang; Zhijun Li; Jianguo Wang (Jixie chuandong)
According to the nonlinear and non-stationary of the planetary gearbox vibration signal fault characteristics is difficult to extract and the problem of low classification accuracy Gaussian kernel extreme learning machine based on random generation kernel parameters， a method for identifying the state of planetary gearboxes with enhence multi-scale permutation entropy （EMPE） and nuclear-polarized Gaussian kernel extreme learning machine （KELM） is proposed. Firstly， noise reduction of vibration signals of planetary gearbox planetary gears by morphological average filtering and using EMPE to obtain permutation entropy values at multiple scales to constructing eigenvector set. Secondly， kernel parameter σ of Gaussian kernel extreme learning machine is optimized by kernel polarization. Finally， using the EMPE eigenvector set as input， the planetary gear state identification model is constructed by the training of KP-KELM algorithm. The experiment results show that， compared with the fault classification model based on SVM and KELM， the EMPE and KP-KELM planetary gear fault diagnosis method has higher classification accuracy and stronger generalization ability.