Application of artificial intelligence techniques to the study of machine signatures
Chen, W.-Y. Xu, J.-X. Panda, S.K.
Chen, W.-Y.
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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.
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Proceedings - 2012 20th International Conference on Electrical Machines, ICEM 2012
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Date
2012
DOI
10.1109/ICElMach.2012.6350218
Type
Conference Paper