Diagnosis of Short‐Circuit Faults in Stator Winding Inside Low‐Voltage Induction Motor Using Impulse Voltage Test

Abstract

SUMMARY Short‐circuit faults in windings due to the deterioration of insulation is among the most common faults in motor drive systems. An easy and effective fault diagnosis method is urgently required to ensure highly reliable operation. This paper proposes a novel method for the diagnosis of short‐circuit faults in stator winding inside a low‐voltage induction motor without removing the rotor, by performing an impulse voltage test. As the rotor does not need to be removed from the motor in this novel diagnosis method, the method can be put to practical use. In this study, first, several impulse voltage tests are carried out on the stator windings of motors. Second, the values of two features that represent the characteristics corresponding to the condition of the motor are calculated, and it is found that the shape of the feature distribution does not depend on the rotor position but on the condition of the winding. Third, the distance between the feature distribution for the healthy motor and features obtained from a target motor is calculated. On the basis of this distance, the condition of the stator winding inside the induction motor is determined. The effectiveness of the proposed diagnosis method is verified by performing experiments that involve several motors with healthy and faulty windings.

Prony Method Estimation as a New Approach for Surge Comparison Testing in Turn Insulation Diagnostics for Three Phase Stator Windings

“…Some of the advances and recent research in SCT have been presented in the literature, where several methods and techniques are evaluated. For example, in [6], a sensitivity analysis of EAR index ratio including the examination of zero crossings of the waveform is proposed; in [7], the parameter identification of the equivalent circuit constants considering an identification environment and diagnosis algorithm is proposed; in [8], an analysis is performed using the zero crossing time (ZCT) signal of the stator current for detection of short circuit faults by detecting a weak turn insulation; in [9], a wavelet transform (WT) and artificial neural network (ANN) approach is used to detect and classify faults based on features extracted from high frequency measurements of the admittance, current, or voltage; also, in [10], an evaluation of motor insulation using a classifier based on ANN is performed; in [11], a transient model for an induction machine with stator winding turn faults and the steady-state equivalent circuits are presented, from which the sequence components of the line currents can be estimated as a function of fault severity, and in [12], online surge testing is proposed. Also, other parametric methods such as the generalized likelihood ratio test (GLRT), maximum likelihood estimation (MLE) and the subspace spectral estimation technique reduces the noise effects on parameter estimation results.…”