An Application of Support Vector Machines for Induction Motor Fault Diagnosis with Using Genetic Algorithm

This paper introduces a technique for diagnosing mechanical faults of induction motors by using support vector machine (SVM) and genetic algorithm (GA). Features are extracted from the vibration time signals and selected by using GA with a distance evaluation fitness function. All SVM parameters are also obtained simultaneously by the same GA. The SVM is studied with two types of kernel functions, the radial basis function and the polynomial function. Four motor conditions are investigated with the chosen SVM classifiers. The classification results have high accuracy for the chosen feature set and SVM parameters.

[1]  J. S. Rao,et al.  Vibratory Condition Monitoring of Machines , 2000 .

[2]  Sheng-Fa Yuan,et al.  Fault diagnostics based on particle swarm optimisation and support vector machines , 2007 .

[3]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[4]  Bo-Suk Yang,et al.  Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors , 2007, Expert Syst. Appl..

[5]  Jin Chen,et al.  Decision tree and PCA-based fault diagnosis of rotating machinery , 2007 .

[6]  Alireza Sadeghian,et al.  Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System , 2006 .

[7]  K. I. Ramachandran,et al.  Feature selection using Decision Tree and classification through Proximal Support Vector Machine for fault diagnostics of roller bearing , 2007 .

[8]  Biswanath Samanta,et al.  Artificial neural networks and genetic algorithm for bearing fault detection , 2006, Soft Comput..

[9]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[10]  Yu Yang,et al.  A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM , 2007 .

[11]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs , 2007 .

[12]  Guy Clerc,et al.  The use of features selection and nearest neighbors rule for faults diagnostic in induction motors , 2006, Eng. Appl. Artif. Intell..

[13]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .