Abstract
Bearing fault diagnosis has attracted significant attention over the past few decades. In this paper, Alpha-stable distribution was introduced for feature extraction from faulty bearing vibration signals. Such a non-Gaussian model can accurately describe statistical characteristic of bearing fault signals with impulsive behavior. After extracting feature vectors by Alpha-stable distribution parameters, the weighted support vector machine (wSVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
