Fault diagnosis of rolling bearing using symmetrized dot pattern and density-based clustering

The rolling bearing usually works under complex working conditions, which makes it more susceptible to mechanical failure. The vibration signals are usually complex, nonlinear and non-stationary. This paper proposed a novel diagnosis method for rolling bearing combined with the adaptive symmetrized dot pattern and density-based spatial clustering of applications with noise (ASDP-DBSCAN). Firstly, the SDP technique is briefly introduced and then the vibration signals are reconstructed by the SDP pattern. Secondly, in order to maximize the difference between SDP patterns, a novel parameter optimization method of SDP pattern is presented combined with Hill function and genetic algorithm (HFGA), which is conducive to improve diagnostic accuracy. Then, an improved DBSCAN is used to generate clustering template so as to reduce the effect of noise on diagnostic accuracy. Furthermore, the similarity analysis between clustering template and unknown SDP pattern is used to fault classification. Finally, the proposed method is applied to fault diagnosis for the rolling bearing. The experimental results validate that the proposed method is more effective than other methods for rolling bearing fault diagnosis.