Application of artificial intelligence techniques to the study of machine signatures

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.