Abstract
Roller element bearing is an important part of mine ventilating fan. The management and maintenance of the equipment is very important. Therefore, it is necessary to employ fault diagnosis process to the roller element bearing. In this paper, mechanics properties of roller element bearing are analyzed. Then, Radial Basis Function (RBF) neural network is used for the fault diagnosis of the roller element bearing. The structure and inference of RBF network are discussed in detail. The roller element bearing fault diagnosis model is established based on RBF network. A case study is given. The proposed method is applied to the fault diagnosis of roller element bearing. The result shows that the proposed method can improve efficiency of the fault diagnosis.
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.
