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|Title:||Application of artificial intelligence techniques to the study of machine signatures||Authors:||Chen, W.-Y.
|Issue Date:||2012||Citation:||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|
|Appears in Collections:||Staff Publications|
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