Abstract
An identification method of spindle bearings fault is proposed in the article. According to the experimental data of continuous attribute, k-means clustering method is introduced into data discretization of continuous attributes. The condition attributes in decision table are reducted by discernibility matrix algorithm. In this way, simple and clear fault pattern rules are obtained. The result indicates that the method can realize fault pattern identification of spindle's bearings and it is of great application value in practical fault pattern identification.
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.
