Fault diagnosis of rolling bearing based on kurtosis criterion VMD and modulo square threshold

Abstract

Aiming at the problem that fault signals of rolling bearing are easily submerged by the strong background noise, which makes it difficult to extract fault information, so a method based on kurtosis criterion variational mode decomposition (VMD) and modulo square threshold is proposed and applied to fault diagnosis of rolling bearing. First, the vibration signals of rolling bearing are processed by VMD. Second, the signals are reconstructed by selecting the intrinsic mode function (IMF) components with the largest kurtosis and the second largest kurtosis. Finally, the reconstruction signals are de-noised by the modulo square threshold. Through the test of the inner ring and outer ring of the rolling bearing, the feasibility and effectiveness of the proposed method are verified.

Keywords

• signal processing
• vibrations
• fault diagnosis
• mechanical engineering computing
• rolling bearings
• fault diagnosis
• rolling bearing
• kurtosis criterion vmd
• modulo square threshold
• fault signals
• fault information
• kurtosis criterion variational mode decomposition
• largest kurtosis