Research on Bearing Fault Diagnosis Based on Cyclic Statistics

Bearing is an important component of equipment. Different types of failures lead to different changes of the entire equipment and operating parameters. In the urban rail train operation, the quality and status of bearings have a significant impact on traffic safety. In rotary machinery fault diagnosis, second-order cycle statistics has become an important signal analysis tool. In this paper, the early fault point is realized by CUSUM combined with spectral correlation density function, and the characteristics of different fault are analyzed using second-order cyclic statistics. The research of the theory of cyclic stationary provides the direction and power for the development of bearing fault diagnosis with a case.