Detection of mechanical faults in induction motors supplied with adjustable speed drives

In this thesis, the mechanical faults that occur in induction motors, which are widely used in industry due to their simple and rugged construction and cost effective pricing, are analyzed. The certain fault modes are generated synthetically in motors and motors are run by a voltage controlled frequency inverter with four different speed reference and three different load conditions to gather the digitalized current data that measured between the voltage controlled driver and the network in laboratory environment. By applying wavelet packet decomposition, wavelet coefficients are obtained from stator current data and these coefficients are used to obtain feature vectors by using statistical parameters of them. Fault detection is obtained by classifying these feature vectors with different types of classifiers. As a result, correct classification rates are compared not only with faulty and healthy motors, but also with different fault modes. The effects of the speed reference, current level, selected wavelet type in wavelet packet decomposition and type of the classifier in fault detection is also observed.