학술논문

Early fault diagnosis method based on time-domain marginal spectrum of S transform and SVMD for rolling bearings
Document Type
Conference
Source
2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) Mechanical Engineering and Intelligent Manufacturing (WCMEIM), 2022 5th World Conference on. :515-520 Nov, 2022
Subject
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Fault diagnosis
Time-frequency analysis
Adaptation models
Rolling bearings
Transforms
Feature extraction
Manufacturing
rolling bearing
successive variational mode decomposition (SVMD)
S transform
fault diagnosis
Language
Abstract
Aiming at the problem of early weak fault diagnosis for rolling bearings, an early fault diagnosis method based on time-domain marginal spectrum of S transform and successive variational mode decomposition(SVMD) is proposed. Firstly, the S transform is used to process the bearing fault signal and the time-domain marginal spectrum is extracted. Then time-domain marginal spectrum S transform is decomposed adaptively by using SVMD and the IMF components which are close to the bearing fault feature frequency are automatically selected for reconstruction. Finally, spectrum analysis of the reconstructed time-domain marginal spectrum of S transform is employed to realize bearing fault diagnosis. Experimental results show that the proposed method can extract weak fault feature components effectively, thereby significantly improving early fault diagnosis accuracy for rolling bearings.