Fault diagnosis for quayside container crane reducer based on EEMD decomposition and PCA-FCM clustering

Abstract

Aiming at the feature extraction and fault diagnosis issue of quayside container crane(quayside crane) reducer, a combination method of reducer fault diagnosis based on ensemble empirical mode decomposition(EEMD) and principal component analysis(PCA)-fuzzy C-means(FCM) clustering is proposed. Firstly, the nonlinear and non-stationary vibration signals of the reducer are decomposed into several intrinsic mode functions(IMF) by EEMD decomposition, and the multi-dimensional fault characteristics of each IMF component are extracted. Then, the principal component analysis method is used to visually reduce the dimension of the fault feature, the relationship between the characteristic value of the vibration signal of the reducer and the fault mode is analyzed, and the state of the reducer is identified by the fuzzy C-means clustering algorithm. The experimental results show that EEMD-PCA-FCM method has high recognition accuracy for the three states of the reducer, which indicates that the method is an accurate and effective reducer fault diagnosis method.

Keywords

• fault diagnosis
• quayside container crane reducer
• ensemble empirical mode decomposition(eemd)
• fuzzy c-means clustering
• principal component analysis(pca)