DWT Analysis of Numerical and Experimental Data for the Diagnosis of Dynamic Eccentricities in Induction Motors

[EN] The behaviour of an induction machine during a startup transient can provide useful information for the diagnosis of electromechanical faults. During this process, the machine works under high stresses and the effects of the faults may also be larger than those in steady-state. These facts may help to amplify the magnitude of the indicators of some incipient faults. In addition, fault components with frequencies dependant on the slip evolve in a particular way during that transient, a fact that allows the diagnosis of the corresponding fault and the discrimination between different faults. The discrete wavelet transform (DWT) is an ideal tool for analysing signals with frequency spectrum variable in time. Some research works have applied with success the DWT to the stator startup current in order to diagnose the presence of broken rotor bars in induction machines. However, few works have used this technique for the study of other common faults, such as eccentricities. In this work, time-frequency analysis of the stator startup current is carried out in order to detect the presence of dynamic eccentricities in an induction motor. For this purpose, the DWT is applied and wavelet signals at different levels are studied. Data are obtained from simulations, using a finite element (FE) model of an induction motor, which allows forcing several kinds of faults in the machine, and also from experimental tests. The results show the validity of the approach for detecting the fault and discriminating with respect to other failures, presenting for certain applications (or working conditions) some advantages over the traditional stationary analysis. (c) 2007 Elsevier Ltd. All rights reserved.Antonino-Daviu, J.; Jover Rodriguez, P.; Riera-Guasp, M.; Arkkio, A.; Roger-Folch, J. (2007). DWT Analysis of Numerical and Experimental Data for the Diagnosis of Dynamic Eccentricities in Induction Motors. Mechanical Systems and Signal Processing. 21(6):2575-2589. doi:10.1016/j.ymssp.2007.01.0082575258921