Journal of Control, Automation and Electrical Systems (2018) 29:489–499
https://doi.org/10.1007/s40313-018-0388-5
A Neuro-Fuzzy Approach for Locating Broken Rotor Bars in Induction
Motors at Very Low Slip
Cleber Gustavo Dias
1
· Cristiano Morais de Sousa
1
Received: 22 October 2017 / Revised: 17 March 2018 / Accepted: 17 May 2018 / Published online: 30 May 2018
© Brazilian Society for Automatics–SBA 2018
Abstract
Squirrel-cage induction motors are widely used in a number of applications throughout the world. This paper proposes a
neuro-fuzzy approach to identify and to classify a typical fault related to the induction motor damage, such as broken rotor
bars. Two fuzzy classifiers are obtained by an adaptive-network-based fuzzy inference system model whose parameters can be
identified by using the hybrid learning algorithm. A Hall effect sensor was installed between two stator slots of the induction
machine, and a magnetic flux density variation is measured according to the failure. The data from the Hall sensor were
used to extract some harmonic components by applying fast Fourier transform. Thus, some frequencies and their amplitudes
were considered as inputs for the proposed fuzzy model to detect not only adjacent broken bars, but also noncontiguous
faulted scenarios. In the present work it is not necessary to estimate the rotor slip, as required by the traditional condition
monitoring technique, known as motor current signature analysis. This method was able to detect broken bars for induction
motor running at low-load or no-load condition. The intelligent approach was validated using some experimental data from
a 7.5-kW squirrel-cage induction machine.
Keywords Broken rotor bars · Neuro-fuzzy · Induction motor · Fault diagnosis · Low slip · Hall sensor
1 Introduction
Squirrel-cage induction motors (SCIMs) are currently
responsible for several tasks, including pumps, fans and com-
pressors, for example. Induction motors are also applied to
many fields, such as petrochemical, pulp and paper indus-
tries as shown by Zhang et al. (2011), as well as applications
in electrical vehicles, as described by Guan et al. (2014).
According to Sauer et al. (2015), induction motors are
responsible for 35% of the electrical energy consumption
in Brazil. Other countries, such as Canada, the USA, India
and the European Union, have more than 70% of energy con-
sumption related to induction motor usage.
Although SCIMs are robust machines due to their reli-
able and simple construction, in many cases these motors
are subjected to relevant mechanical, thermal and electri-
cal strengths. In the works by Jung et al. (2006), Zhang
B
Cleber Gustavo Dias
diascg@uni9.pro.br
1
Informatics and Knowledge Management Graduate Program,
Nove de Julho University - UNINOVE, Rua Vergueiro,
n.235/249, Liberdade, São Paulo, SP, Brazil
et al. (2011), Chandralekha and Jayanthi (2016), Sauer
et al. (2015), Palacios et al. (2016), Naha et al. (2016) and
Lizarraga-Morales et al. (2017) some techniques are pre-
sented to detect faults in electrical machines, such as short
circuit in the stator coils, bearing faults and broken rotor bars.
For this reason, motor fault diagnosis is usually applied
to predictive maintenance programs in the industry. In recent
years, condition monitoring solutions have been investigated
to identify some electrical and mechanical faults in induction
motors.
Broken rotor bars are a typical failure that may occur
during motor operation in both transient and steady-state con-
ditions. Many researches have invested time and resources to
detect and to classify broken bars, in order to avoid downtime
and high maintenance costs. Some recent studies have shown
a concern about detecting incipient rotor faults, such as the
works by Chandralekha and Jayanthi (2016), Sauer et al.
(2015), Palacios et al. (2016), Naha et al. (2016) , Lizarraga-
Morales et al. (2017) and el Malek et al. (2017).
According to Lee et al. (2015), motor current signature
analysis (MCSA) is currently applied for broken bars detec-
tion and this technique has been used over the last decades,
particularly due to its noninvasive characteristics and attrac-
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