Abstract
In this paper, a new fuzzy C-mean (FCM) clustering method based on refined composite multiscale fuzzy entropy (RCMFE) is proposed and applied to the bearing fault diagnosis of rotating machinery. The RCMFE adds multiscale processing, which can not only characterize the complex changes of time sequences at several different scales but also solve the problems of information loss caused by the process of coarse granulation and large fluctuation of entropy value when the scale becomes large. This method uses RCMFE to extract fault features, that are inputted into the Fuzzy C-mean clustering algorithm to achieve rolling bearing fault identification. Comparing the method with Multiscale Fuzzy Entropy (MFE) and Composite Multiscale Fuzzy Entropy (CMFE), the method used was experimentally proven to have better diagnostic results.
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.
