Abstract
Noise emitted during test runs of mitre-gear units was analyzed using a neural network based tool. Gears can be classified dependent on how noise levels vary with changes of revolution speed. Faults can be detected and necessary adjustments can be identified. The kernel of the work is the appropriate selection, reduction and preprocessing of input data, parameterization of the clustering (unsupervised learning) algorithms, and the building of the classification model (supervised learning).
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.