Rotating machinery fault diagnosis for imbalanced data based on decision tree and fast clustering algorithm

Abstract

To diagnose rotating machinery fault for imbalanced data, a kind of method based on fast clustering algorithm and decision tree is proposed. Combined with wavelet packet decomposition and isometric mapping (Isomap), sensitive features of different faults can be obtained so the imbalanced fault sample set is constituted. Then the fast clustering algorithm is applied to search core samples from the majority data of the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data is built. After that, decision tree is trained with the balanced fault sample set to get the fault diagnosis model. Finally, gearbox fault data set and rolling bearing fault data set are used to test the fault diagnosis model. The experiment results show that proposed fault diagnosis model could accurately diagnose the rotating machinery fault for imbalanced data.