Abstract
A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.
Highlights
In the theory of mechanical equipment maintenance, condition-based maintenance (CBM) is proposed based on the real-time monitoring information of the equipment
Rolling bearing is an important rotary supporting component in mechanical equipment and bearing faults usually take up about 30% in rotating machinery faults
Ensemble empirical mode decomposition (EEMD) permutation entropy is extracted as the fault feature,[25] and principal component analysis and GK clustering method are employed to achieve automatic clustering for rolling bearings
Summary
In the theory of mechanical equipment maintenance, condition-based maintenance (CBM) is proposed based on the real-time monitoring information of the equipment. Keywords Basic scale entropy, feature extraction, Gath-Geva fuzzy clustering, rolling bearing, condition recognition
Sign-in/Register to view highlights & summary 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.
