Incipient Bearing Fault Extraction based on an Adaptive Multi-stage Noise Reduction Method

• Published in: Journal of physics. Conference series Vol. 2762; no. 1; pp. 012074 - 12087
• Main Authors: Tian, Shaoning, Feng, Guojin, Meng, Zhaozong, Liu, Xiaoang, Zhen, Dong, Gu, Fengshou
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.05.2024
• Subjects: Adaptive variational mode decomposition; Decomposition; Fault diagnosis; Kurtosis; Modulation signal bispectrum; Noise reduction; Parameters; Roller bearings; Rolling bearing; Vibration
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2762/1/012074

Considering the strong nonlinear and non-stationary characteristics of rolling bearing vibration signals, this paper proposes a multi-stage noise reduction method using adaptive variational mode decomposition and modulation signal bispectrum (AVMD-MSB) to extract the fault features of rolling bearings. Firstly, the AVMD is employed to adaptively select VMD parameters K and α and decompose the signal into a series of Intrinsic mode functions (IMFs), which allows an adaptive selection of the parameters of VMD. Then, all IMF components are reconstructed with weights according to the index of correlation kurtosis to avoid accidental omission of the IMFs containing important fault information. Finally, MSB is implemented to further suppress residual noises and interference components in the signal, precisely extract the bearing fault features. Numerical simulation and case study show that the AVMD-MSB is more advantageous in extracting fault characteristics from rolling bearing vibration signals compared with AVMD-Envelope and conventional VMD-MSB.