Design, implementation and comparison of two wavelet based methods for the detection of broken rotor bars in three phase induction motors

2010 ◽  
Author(s):  
R. Salehi Arashloo ◽  
A. Jalilian
Keyword(s):  
Induction Motors ◽  
Broken Rotor Bars ◽  
Three Phase

scholarly journals Induction Motor Faults Detection Using a Statistical Procedure Based-Approach

2017 ◽  
Vol 2 (3) ◽  
pp. 130-139
Author(s):  
A. Kouadri ◽  
A. Kheldoun ◽  
M. Hamadache ◽  
L. Refoufi
Keyword(s):  
Induction Motors ◽  
Random Variable ◽  
Detection Algorithm ◽  
Statistical Procedure ◽  
Distribution Law ◽  
Broken Rotor Bars ◽  
Statistical Estimates ◽  
Three Phase ◽  
A New Technique ◽  
Stator Resistance

This paper presents the application of a new technique based on the variance of three phase stator currents’ instantaneous variance (VIV-TPSC) to detect faults in induction motors. The proposed fault detection algorithm is based on computation of the confidence interval index (CI) at different load conditions. This index provides an estimate of the amount of error in the considered data and determines the accuracy of the computed statistical estimates. The algorithm offers the advantage of being able to detect faults, particularly broken rotor bars, independently of loading conditions. Moreover, the implementation of the algorithm requires only the calculation of the variance of the measured three-phase stator currents’ instantaneous variance. The discrimination between faulty and healthy operations is based on the adherence of VIV-TPSC value to the CI which is calculated after checking out that the variance of instantaneous variance is a random variable obeying to normal distribution law. Rotor and stator resistance values are not used in any part of the CI and VIV-TPSC calculations, giving the algorithm more robustness. The effectiveness and the accuracy of the proposed approach are shown under different faulty operations.

Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

2016 ◽  
Vol 10 (5) ◽  
pp. 430-439 ◽  
Author(s):  
Wagner Fontes Godoy ◽  
Ivan Nunes Silva ◽  
Alessandro Goedtel ◽  
Rodrigo Henrique Cunha Palácios ◽  
Tiago Drummond Lopes
Keyword(s):  
Induction Motors ◽  
Broken Rotor Bars ◽  
Three Phase ◽  
Intelligent Tools

A New Acoustic Emission-based Approach for Supply Disturbances Evaluation in Three-Phase Induction Motors

2020 ◽  
pp. 1-1
Author(s):  
Guilherme Beraldi Lucas ◽  
Bruno Albuquerque De Castro ◽  
Marco Aurelio Rocha ◽  
Andre Luiz Andreoli
Keyword(s):  
Acoustic Emission ◽  
Induction Motors ◽  
Three Phase

scholarly journals Mutual Information and Meta-Heuristic Classifiers Applied to Bearing Fault Diagnosis in Three-Phase Induction Motors

2020 ◽  
Vol 11 (1) ◽  
pp. 314
Author(s):  
Gustavo Henrique Bazan ◽  
Alessandro Goedtel ◽  
Marcelo Favoretto Castoldi ◽  
Wagner Fontes Godoy ◽  
Oscar Duque-Perez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Operating Conditions ◽  
Industrial Systems ◽  
Bearing Fault Diagnosis ◽  
Bee Colony ◽  
Three Phase ◽  
C4.5 Decision Tree ◽  
The Time Domain

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.

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