Abstract
This paper presents electric motor fault diagnosis using two kinds of Artificial Neural Networks (ANN): feedforward networks and self organising maps (SOM). Major faults such as bearing faults, stator winding fault, unbalanced rotor and broken rotor bars are considered. The ANNs were trained and tested using measurement data from stator currents and mechanical vibration signals. The effects of different network structures and the training set sizes on the performance of the ANNs are discussed. This study shows that the feedforward ANN with a very simple internal structure can give satisfactory results, while SOMs can classify the type of motor faults during steady state working conditions. The experiment results also show that the feedforward ANN is the more promising scheme in this case where fault data from electric motors is available.
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 callMore From: Insight - Non-Destructive Testing and Condition Monitoring
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.
