Modeling, detection and classification of eccentricity and broken bar faults in electric machines

The objective of this dissertation is the study, modeling, detection and classification of some mechanical faults for electric machines, such as broken rotor bar and eccentricity fault in induction, do and PM motors. In most cases, location of the harmonics affected by faults vary with the rotor speed. Therefore, measuring or estimating the rotor speed is important for diagnostics.; In this dissertation, several speed estimation techniques for induction machines are introduced and the simulation result of a Kalman filtering technique in an indirect vector control system is presented. Detecting the speed harmonic in the armature current signal of a dc motor is introduced as a new method for estimating its rotor speed.; Broken rotor bar of an induction machine is modeled by a novel equivalent electrical circuit. Results show the effect of failure in both amplitude and phase of the fundamental harmonic. Also, it verifies the presence of side band harmonics around the fundamental harmonic when the fault happens. The amplitude of these harmonics depend on load and rotor parameters and vary when the fault severity changes.; A novel method is used to model eccentricity fault for induction machines, dc and PM machines. Analytical results show that in an induction machine, eccentricity fault affects the fundamental and slot harmonics in a non-linear way and generates several extra harmonics. For a dc and PM machine, it is shown that eccentricity generates second harmonics in the back EMF signal. Also, the winding function theory shows that for a dc motor with an eccentric rotor, L_AA is changed that produces several harmonics in the armature current.; A Bayes minimum error classifier is utilized to distinguish between the healthy and faulty conditions of the machine. The experimental results of the designed classifier for fault classification of an induction motor and a dc motor are presented which verify the feasibility of the proposed method and the validity of the utilized features. It also proves the strength of the analysis carried out throughout this dissertation.