Abstract
In recent years, early fault detection and diagnosis of gears have become extremely important due to requirement to decrease the downtime on production machinery caused by the failures. For that reason, researches have been done for the early detection of faults through the analysis of their vibration signals. Modern day machines, due to their complexities, can have many vibration generating sources in addition to noises. Therefore it is important that the vibration signal of faulty gear to be recognized and recovered for the diagnostics. In this paper Back-Propagation neural network has been used for the classification of RPM and oil level related gearbox faults that can occur during operation. With the help of Power spectrum technique, signal was more refined in order to make the feature selection process much more accurate.DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13334
Sign-in/Register to access full text options 
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.
