Article
Broken Rotor Bar Detection in Induction Motors through
Contrast Estimation
Edna Rocio Ferrucho-Alvarez , Ana Laura Martinez-Herrera, Eduardo Cabal-Yepez *, Carlos Rodriguez-Donate,
Misael Lopez-Ramirez and Ruth Ivonne Mata-Chavez
Citation: Ferrucho-Alvarez, E.R.;
Martinez-Herrera, A.L.; Cabal-Yepez,
E.; Rodriguez-Donate, C.; Lopez-
Ramirez, M.; Mata-Chavez, R.I.
Broken Rotor Bar Detection in
Induction Motors through Contrast
Estimation. Sensors 2021, 21, 7446.
https://doi.org/10.3390/s21227446
Academic Editors: Hamed Badihi,
Tao Chen and Ningyun Lu
Received: 17 October 2021
Accepted: 7 November 2021
Published: 9 November 2021
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Attribution (CC BY) license (https://
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4.0/).
Department of Multidisciplinary Studies, Engineering Division, Campus Irapuato-Salamanca,
University of Guanajuato, Av. Universidad S/N, Yacatitas, Yuriria 38944, Mexico;
er.ferruchoalvaez@ugto.mx (E.R.F.-A.); martinez.al@ugto.mx (A.L.M.-H.); c.rodriguezdonate@ugto.mx (C.R.-D.);
lopez.misael@ugto.mx (M.L.-R.); ruth@ugto.mx (R.I.M.-C.)
* Correspondence: educabal@ugto.mx; Tel.: +52-445-458-9040
Abstract:
Induction motors (IM) are key components of any industrial process; hence, it is important
to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions
on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex
to detect faults, have attracted the attention of many researchers, who are searching for reliable
methods to recognize this condition with high certainty. Most proposed techniques in the literature
are applied during the IM startup transient, making it necessary to develop more efficient fault
detection techniques able to carry out fault identification during the IM steady state. In this work, a
novel methodology based on motor current signal analysis and contrast estimation is introduced
for BRB detection. It is worth noting that contrast has mainly been used in image processing for
analyzing texture, and, to the best of the authors’ knowledge, it has never been used for diagnosing
the operative condition of an induction motor. Experimental results from applying the approach
put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and
classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB),
with high certainty during its steady state.
Keywords:
contrast estimation; broken rotor bars; fault diagnosis; fuzzy logic; induction motors;
steady state
1. Introduction
Rotary machines, such as induction motors (IM), have become essential tools for
industrial processes due to their low cost and ruggedness [
1
]. These machines undergo
different types of failures associated with the rotor, the stator, or the bearings due to
distinct operational circumstances. An incipient fault in an IM is usually silent, and it can
generate distinct types of problems, such as interruption of a production line and damage
to surrounding machinery, and, in the worst scenario, it might cause a total collapse of the
system, which would provoke significant economic losses for an industry [
2
,
3
]. Hence,
continuous monitoring of IM is essential for detecting incipient faults in a timely manner
and keeping the industrial processes working properly [4].
The presence of broken rotor bars (BRBs) is quite a difficult condition to detect, since
an IM with this problem continues working without giving any hint about failure. A BRB
starts as a simple crack and evolves until the bar is completely broken [
5
]. At this point,
the IM power consumption increases; therefore, production costs also rise [
6
]. Hence,
BRB detection has remained a subject of interest for researchers and, consequently, a
considerable number of new approaches to BRB identification have emerged in recent
years. These approaches look toward reliable, automatic methods of recognizing this
condition with high certainty and diminishing false alarms [
7
]. However, many techniques
proposed in the literature are invasive, and they require the IM to stop operating, making
Sensors 2021, 21, 7446. https://doi.org/10.3390/s21227446 https://www.mdpi.com/journal/sensors
