Research on fault diagnosis method of walking gearbox of combine harvester based on DA-KELM

Abstract

Aiming at the problem of the low recognition rate of the rolling bearing fault of the walking gearbox of the combine harvester, a gearbox rolling bearing fault diagnosis method based on the dragonfly optimization algorithm kernel extreme learning machine is proposed. The Variational Mode Decomposition(VMD) algorithm optimized by the particle swarm optimization algorithm is used to decompose the experimentally extracted vibration signals of the gearbox in different working states, and the sample entropy value is extracted from the Intrinsic Mode components obtained by the decomposition as the fault characteristic value, and the The time-domain and frequency-domain characteristics of the vibration signal together constitute the fault feature set. The DA-KELM algorithm is used to identify the fault in the feature set of the vibration signal in various states. Through pattern recognition of four states: normal, roller pitting, outer raceway pitting, and inner raceway pitting of the rolling bearing in the traveling gearbox of the combine harvester, The best classification accuracy is 95.625%. At the same time, this method was compared with the common classification algorithm, and the experimental results show that this method has advantages in the accuracy of fault identification.