DIAGNOSIS AND DETECTION OF ECCENTRICITY RELATED MOTOR FAULTS

Abstract

From all kinds of faults, the most common faults that impact the lives and efficiency of rotating motors and generators is air gap eccentricity. As a result, the condition monitoring system is needed to detect the faults in the eccentricity of machines early on. Sensor-based systems are the most popular air gap monitoring systems for synchronous machines, but they are costly and intrusive. The aim of this project was to develop a low-cost, non-inverse air gap monitoring methods, specifically for salient pole synchronous motors. For the aforementioned purpose, motor current signature analysis was more used. If the fault specific frequency elements are identified ahead of time, the defective condition can be isolated by measuring the frequency range of the machine's current. The characteristic frequency elements were then predicted with the aid of a magneto-motive force – precise permeance analysis. Mathematics simulation of a 3 phase salient pole synchronous machine and 3 phase reluctance synchronous machine depend on the changed winding function method were established to verify the hypothesis and define a pattern in the variation of these harmonic components with changing levels of eccentricity. In the models, there were dynamic, static and mixed eccentricity conditions of various failure. To check the theoretical predictions, time stepped finite element based simulations were performed in Maxwell-2D. Experiments were then carried out in the lab with eccentrically cut bushings to test the study eccentricity sensor system. To provide time harmonics, device constructional asymmetry, supply voltage imbalanced, and other non idealities have been found to have a negative impact on diagnostic technique. As a result, a residual estimation-based fault detection scheme for distinguishing eccentricity fault from healthy condition has been successfully introduced. Furthermore, detection logic has been developed to distinguish the form of eccentricity and estimate the fault severity. A detailed overview of the most often used faults found in induction motors is given in the 3rd section of my thesis. The spectrum of faults is categorized by their place: rotor and stator, it will explain on the following: Stator winding faults, Faults in the core of the stator, faults in the rotor windings Faults with the rotor bar, bearings, and gearbox faults, misbalancing of machine wings, wings are not closely linked to the problem of motor and power cord, turn, etc. I have simulated the sound signature analysis for detection of the fault happened in the machine, for my case I have used induction motor.