Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICElMach.2012.6350218
Title: Application of artificial intelligence techniques to the study of machine signatures
Authors: Chen, W.-Y.
Xu, J.-X. 
Panda, S.K. 
Issue Date: 2012
Source: Chen, W.-Y.,Xu, J.-X.,Panda, S.K. (2012). Application of artificial intelligence techniques to the study of machine signatures. Proceedings - 2012 20th International Conference on Electrical Machines, ICEM 2012 : 2390-2396. ScholarBank@NUS Repository. https://doi.org/10.1109/ICElMach.2012.6350218
Abstract: This paper presents demonstration on the application of artificial intelligence techniques to the study of machine vibration signatures. First, a Self-Organizing Map (SOM) is used to discover cluster information from frequency-domain vibration signatures for the detection and diagnosis of unbalanced rotor and bearing faults. In the next, with further feature extraction in frequency-domain, a 2-dimensional multi-class Support Vector Machine (SVM) is used to predict these fault modes with an error rate of 1.48% over a wide machine operation speed. © 2012 IEEE.
Source Title: Proceedings - 2012 20th International Conference on Electrical Machines, ICEM 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/69423
ISBN: 9781467301428
DOI: 10.1109/ICElMach.2012.6350218
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