Life Prediction of the Gear Transmission System with Multicharacteristics

Abstract

This study obtains and predicts multifault data in the key transmission and connection systems with gears. Model building is based on the multikernel extreme learning machine with the method of maximum correlation kurtosis deconvolution and variational mode decomposition. To this end, the realization form of the life prediction is first studied by enhancing the low-frequency signal. Then, the larger correlation coefficient is selected as the sensitive feature parameter aiming at mapping to a feature space by the randomly initialized hidden layer in the learning machine, and the weight value of output layer is obtained using the least square method. A case study on the fault diagnosis of gear transmission system is conducted in the end to illustrate the proposed approach.