Cylindrical roller bearing fault diagnosis based on VMD-SVD and Adaboost classifier method

Abstract

Fault diagnosis for cylindrical roller bearing is of great significance for industry. In order to excavate the features of the vibration signal adequately, and to construct an effective classifier for complex vibration signals, this paper proposed a new fault diagnosis method based on Variational Mode Decomposition (VMD), Singular Value Decomposition (SVD) and Adaboost classifier. Firstly, the VMD was applied to decompose the sampled vibration signal in time-frequency domain. Subsequently, the features were extracted by using SVD. Finally, the constructed Adaboost classifier were employed to fault detection and diagnosis, which were trained by using the extracted features. Experimental data measured in a rotating machinery fault diagnosis experiment platform was used to verify the proposed method. The results demonstrate that the proposed method was effective to detect and diagnose the outer ring fault and rolling element fault in cylindrical roller bearing.