Abstract
Rolling bearings are widely used in coal mine equipment, real-time monitoring of bearing status and intelligent diagnosis of bearing fault is very important to the safe and efficient mining of coal mine. In order to solve the problem of insufficient representation ability about fault diagnosis model, a multi-scale segmentation attention-based residual network is proposed for the fault diagnosis of rolling bearings, which can fully and accurately extract vibration signal features to realize intelligent diagnosis. For time-frequency images of vibration signals, residual networks was used for feature extraction. Furthermore, the pyramid split attention mechanism was combined to optimize the feature selection to construct the intelligent diagnosis model for bearing. The proposed method has been applied to the task of fault diagnosis on SKF6205 bearing, and the experimental results show its superior diagnostic performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.