Fault Diagnosis of Crane Gearbox based on Variational Mode Decomposition and PSO-SVM

Abstract

The vibration signal of crane gearbox has the characteristics of low signal-to-noise ratio and nonlinearity，so it needs some professional knowledge and experience to realize fault diagnosis. In order to realize intelligent fault diagnosis of crane gearbox，an intelligent fault diagnosis method based on variational modal decomposition（VMD） improved wavelet denoising and particle swarm optimization（PSO） support vector machine（SVM） is proposed. Firstly， VMD is used to decompose the vibration signal to obtain the intrinsic mode function（IMF） of different scales. The decomposed high frequency component is improved after wavelet de-noising and the low frequency component is reconstructed. Then the feature parameters of reconstructed signal are extracted to construct the feature vector， and kernel principal component analysis（KPCA） is used to realize the feature information fusion. Finally， the PSO optimized SVM is used for fault identification and classification. The experimental results show that the SVM model based on VMD improved wavelet signal preprocessing and PSO algorithm has high recognition accuracy and can effectively and accurately identify and classify the fault types of the crane gearbox.

Keywords

• Crane gearbox;Variational mode decomposition;Wavelet decomposition;Particle swarm optimization;Support vector machine