Abstract

AbstractIn this paper, a method based on the convolutional neural network (CNN) is proposed to effectively identify the crack location in the rotor system. Firstly, a dual-disks hollow shaft rotor system model with a breathing crack is established based on the finite element method. Then the dynamic response of the cracked rotor system is obtained by the 4th harmonic balance method (HBM) and the analysis results show that the super-harmonic resonance of the rotor system is closely related to the depth and location of the crack. Finally, the CNN is adopted to identify the crack position in the rotor system and it can achieve high accuracy. To understand the CNN, we visualize the features space and try to explain the reasons of the great performance of the CNN.KeywordsCrack rotor systemConvolutional neural networksCrack position 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 call

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.