scholarly journals Noise and Vibration Monitoring for Premium Efficiency IE 3 Three-Phase Induction Motors

2015 ◽  
Vol 15 (3) ◽  
pp. 117-122 ◽  
Author(s):  
C. G. NISTOR ◽  
G. SCUTARU ◽  
R. CAMPEANU ◽  
M. CERNAT
Keyword(s):  
Induction Motors ◽  
Vibration Monitoring ◽  
Noise And Vibration ◽  
Three Phase

scholarly journals Analysis of Combined Motor Current Signature and Vibration-Monitoring Techniques in the Study of Broken Bars in Three-Phase High-Performance Induction Motors

2017 ◽  
Vol 06 (03) ◽  
Author(s):  
Ronaldo Siqueira da Gama ◽  
Caio Cesar Jacob Silva ◽  
Carlos Eduardo Ferreira Nascimento ◽  
Alexandre Messias da Silva ◽  
Cesar da Costa
Keyword(s):  
High Performance ◽  
Induction Motors ◽  
Vibration Monitoring ◽  
Monitoring Techniques ◽  
Motor Current ◽  
Three Phase ◽  
Current Signature

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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