Comparative Analysis between PI and Wavelet Transform for the Fault Detection in Induction Motor

Squirrel cage Induction motor is widely used in industries because roughest construction, highly reliable, low cost, high efficiency, user friendly and maintenance is minimum as compare to other motor. Induction motor monitoring has a challenging task for researcher engineers' and industries. In this paper we will discusses the fundamental fault in induction motor. The PI & wavelet transform is considered the most popular fault detection method now a day because it can easy detect the common fault in induction machine such as turn to turn s/c, broken rotor bar, bearing deterioration & open circuit faults etc. According to IEEE-IAS most severe fault is bearing fault (44%), then second is stator winding fault (26%), and last is rotor broken bar fault (8%) and other fault is (22%). Another survey according to Allianz most severe fault is stator winding fault (66%), then rotor fault (13%) and bearing fault is (13%) and other is (13%). (4). There are many methods for detection the fault basically conventional method and other is signal processing technique. Automatic fault detection is widely used in industries because save the maintenance time and money. The overall problems are subdivided into two distinct key modules: (a) operation and control, (b) fault diagnosis. In this paper we proposed a method of comparative analysis between PI & Wavelet Controller for fault detection in Induction motor and find out which one is the best. In this paper we consider only two faults: (a) Broken rotor bar fault, (b) short stator winding fault. Basically bearing is the outer portion of the motor so bearing fault detection is easy as compare to short stator winding fault or broken rotor bar fault.