Title :
Application of artificial intelligence techniques to the study of machine signatures
Author :
Chen, W.-Y. ; Xu, J.-X. ; Panda, S.K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
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.
Keywords :
artificial intelligence; electric machine analysis computing; fault diagnosis; feature extraction; frequency-domain analysis; rotors; support vector machines; 2D multiclass SVM; SOM; artificial intelligence techniques; bearing fault detection; bearing fault diagnosis; cluster information; fault modes; feature extraction; frequency-domain vibration signatures; machine vibration signatures; self-organizing map; two-dimensional multiclass support vector machine; unbalanced rotor detection; unbalanced rotor diagnosis; Feature extraction; Frequency domain analysis; Rotors; Support vector machines; Vectors; Vibrations; Wiener filters;
Conference_Titel :
Electrical Machines (ICEM), 2012 XXth International Conference on
Conference_Location :
Marseille
Print_ISBN :
978-1-4673-0143-5
Electronic_ISBN :
978-1-4673-0141-1
DOI :
10.1109/ICElMach.2012.6350218
