Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM

Abstract

The characteristics of non-smoothness and uncertainty of gearbox fault vibration signal lead to the low accuracy of gearbox fault diagnosis. To address this problem, a gearbox fault diagnosis method based on local mean decomposition (LMD) cloud model feature extraction combined with particle swarm optimization (PSO) kernel extreme learning machine (KELM) is proposed. Firstly, the fault vibration signal is decomposed by LMD to obtain several PF components, and the PF components with higher correlation are screened out using the correlation coefficient principle. Secondly, the screened PF components are input into the cloud model, and the feature vectors are extracted using the inverse cloud generator and input into PSO-KELM for fault diagnosis. Finally, the performance of the method is analyzed using the measured data of the QPZZ-Ⅱ test-bed gearbox. The results show that the recognition accuracy of the method is 97.65%, and compared with various methods this method has the best recognition performance.

Keywords

• Gearbox;Fault diagnosis;Local mean decomposition;Cloud model;Particle swarm optimization kernel extreme learning machine