External fault identification experienced by 3 phase induction motor using PSVM
Fault diagnosis and condition assessment (FDCA) of rotating machines becomes important due to the age of machine in service. Proper FDCA enhance the machine's operational life, efficiency and reducing catastrophic failure. This paper describes a realistic FDCA method for three phase induction motors (IMs) using readily available data. External faults experienced by IM are monitored by proximal support vector machine (PSVM) and compared its performance with standard support vector machine and artificial neural network which revealed that PSVM algorithm is quite faster in investigations leading to reduction in computational load. RMS value of 3 phase voltages and currents are utilized as input variable in PSVM model to identify six types of external faults experienced by IM and normal operating (NF) condition. Testing analysis of 160 samples has been carried out to represent the robustness of the investigated seven status conditions for wide changes in operating and loading condition perturbation.
2014 6th IEEE Power India International Conference (PIICON)