Stator Fault Detection in Induction Motors by Autoregressive Modeling

Abstract

This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR) model. The proposed algorithm is based on instantaneous space phasor (ISP) module of stator currents, which are mapped toα-βstator-fixed reference frame; then, the module is obtained, and the coefficients of the AR model for such module are estimated and evaluated by order selection criterion, which is used as fault signature. For comparative purposes, a spectral analysis of the ISP module by Discrete Fourier Transform (DFT) is performed; a comparison of both methodologies is obtained. To demonstrate the suitability of the proposed methodology for detecting and quantifying incipient short circuit stator faults, an induction motor was altered to induce different-degree fault scenarios during experimentation.

Current Characteristics Analysis and Fault Injection of an Early Weak Fault in Broken Rotor Bar of Traction Motor

“…The stator current is represented by the fault injection module according to (22); the current is composed of four parts. The Ramp in the module is the value of A; Fcn realizes the calculation of damage factor D in (8); Fcn1 implements the calculation of (1/2)( /( − )); In1 represents the input fundamental current.…”

“…Subsystem is used to measure the fundamental current amplitude 1 . Multiply the output of In the same way, when the traction motor encounters other types of faults, such as stator winding interturn shortcircuit fault, air gap eccentrici, and bearing fault [21,22], the stator current will also generate the corresponding frequency current with fault characteristics. Therefore, by changing the second and third parts of (22) and modifying the fault injection module of Figure 5, the fault injection of other fault types or multiple faults (multiple faults occur simultaneously) can be realized.…”

Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors

“…From Table 4, it can be observed that the proposed methodology presents an effectiveness percentage of 98% for detecting the SWSC fault, considering both different severity levels (10, 20, 30 and 40 short-circuited turns) and different mechanical load levels (0%, 33%, 66% and 100%), unlike other works reviewed in the literature [12,19,24], which present mainly the analysis of either a level of damage and different operating conditions or different levels of damage and a constant load operating condition.…”

“…Particularly, MCSA for SWF detection is employed to identify frequencies around the fundamental frequency or harmonic components [15]. In the literature, diverse signal processing algorithms for stator winding short-circuit (SWSC) fault detection using MCSA have been introduced; for instance, fast Fourier transform (FFT) [14,16,17], wavelet transform (WT) [18][19][20], empirical mode decomposition-based methods (EMD) [21,22], Wigner-Ville distribution (WVD) [22], Hilbert transform (HT) [23], statistical time series model (STSM) [24], and statistical analysis (SA) [25]. Despite obtaining promising results, diverse limitations still remain.…”

Modelado y Análisis de Sintonización de Velocidad de un MSIP con Presencia de Fisura Mediante Algoritmos Genéticos

“…Se observa que el modelo dinámico del MSIP con presencia de degradación en la inercia propuesto presenta comportamientos altamente no lineales, lo que conlleva a que la sintonización sea bastante complicada para los parámetros característicos, es por ello que se optó por la sintonización mediante algoritmos genéticos (Villalobos-Piña F. et al, 2015).…”