Monitoring of Induction Motor Mechanical and Electrical Faults by Optimum Multiclass-Support Vector Machine Algorithms Using Genetic Algorithm

The induction motor (IM) may lose their normal efficiency and finally fail due to chronic mechanical or electrical faults or both. For the prevention of failure, the early detection of these faults is necessary. The vibration and current signals are measured and collected for varying speeds and load conditions of IMs from an experimental laboratory test rig. Experiments are conducted for four different mechanical fault conditions and five electrical fault conditions including one intact condition. The identification of fault predictions is studied by considering of all mechanical faults, electrical faults and no fault condition. The one-against-one Multiclass-Support Vector Machine Algorithms (MSVM) with radial basis function (RBF) kernel has been trained at various operating conditions of IMs and predictions performance is presented. Two MSVM algorithms, C-SVM and nu-SVM, are used for the investigation. The RBF kernel parameter (gama) and MSVM parameter (C and nu) are optimally selected by the Genetic Algorithm (GA) for better performance for each case. Prediction performances are presented for different speeds and load conditions.