Chen, W.-Y.Xu, J.-X.Panda, S.K.ELECTRICAL & COMPUTER ENGINEERING2014-06-192014-06-192012Chen, 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. <a href="https://doi.org/10.1109/ICElMach.2012.6350218" target="_blank">https://doi.org/10.1109/ICElMach.2012.6350218</a>9781467301428https://scholarbank.nus.edu.sg/handle/10635/69423This 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.Conference PaperNOT_IN_WOS