Airgap eccentricity fault diagnosis, in three-phase induction motors, by the complex apparent power signature analysis

Abstract

This paper introduces the use of the complex apparent power signature analysis as a tool for the diagnostics of mixed airgap eccentricity condition in operating three-phase squirrel cage induction motors. Firstly, a modelling and simulation study concerning the occurrence of airgap eccentricity in three-phase induction motors is presented. For that purpose, the Winding Function Approach is considered. Then, both simulation and experimental results are presented to illustrate the effectiveness of the proposed approach.

Diagnosis of eccentricity and broken rotor bar related faults of induction motor by means of motor current signature analysis

“…Presence of this type of fault may result in a damage of the stator and rotor. Making the simulation and analyzing the obtained results, first of all it is needed to obtain eccentricity-related harmonics, based on (2). In this case m = 1, s = 0.04, p = 2.…”

“…Since the time of the first application they have become crucial for any industrial system in a wide range of power, starting from few watts and up to several megawatts. Threephase induction motors have gained good reputation and popularity because of relative simplicity, low price, and robust construction [1], [2].…”

Rotor Cage Fault Detection in Induction Motors by Motor Current Demodulation Analysis

“…The rotor cage faults can also lead to the mechanical stresses, shaft torques, bearing failures and therefore air gap dynamic eccentricity (Joksimovic, 2005;Drif & Cardoso, 2008). So the dynamic eccentricity, as a mechanical fault, closely relates to the rotor cage faults.…”

AR model of the torque signal for mechanical induction motor faults detection and diagnosis

“…However, distinguishing misalignment is a challenging task using vibration signals alone [7]. In view of the limitations of vibrations signals, many recent studies uses different signals such as stator current [8], acoustic emission [9], thermal [10], magnetic flux [1], apparent power [11] and electromagnetic or mechanical torque [7] [12]. This last one is most suitable for mechanical and load faults detection since it contain the primary effects of such faults.…”