Walsh-Hadamard Domain-Based Intelligent Online Fault Diagnosis of Broken Rotor Bars in Induction Motors

2022 ◽  
pp. 1-1
Author(s):  
Misael Lopez-Ramirez ◽  
Carlos Rodriguez-Donate ◽  
Luis M. Ledesma-Carrillo ◽  
Francisco J. Villalobos-Pina ◽  
Jorge U. Munoz-Minjares ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Broken Rotor Bars

Fault Signature Analysis Based on Transient Current for Induction Motors

2010 ◽  
Vol 44-47 ◽  
pp. 1807-1811
Author(s):  
Feng Lv ◽  
Hao Sun ◽  
Wen Xia Du ◽  
Shue Li
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Scale Transformation ◽  
Stationary Condition ◽  
Analysis Method ◽  
Broken Rotor Bars ◽  
Multi Scale ◽  
Stator Current ◽  
Time Varying Frequency ◽  
Basic Characteristics

The characteristics of broken rotor bars in induction motors are reflected in the abnormal harmonic of the stator current. At present, fast Fourier transform( ) and time-varying frequency spectrum analysis method are used in such fault diagnosis, but non-stationary motors operation can bring a certain difficulties to the monitoring and diagnosis. This paper studies the basic characteristics of wavelet transform, adopting the wavelet analysis technologies of signal processing and selecting mother wavelet, the paper makes the multi-scale transformation to the motor starting current, excavates the harmonic informations on non-stationary condition, realizes fault diagnosis of motor broken rotor bars effectively, The consistent diagnostic results prove the effectiveness of the method.

Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform

2012 ◽  
Vol 30 ◽  
pp. 131-145 ◽  
Author(s):  
Bashir Mahdi Ebrahimi ◽  
Jawad Faiz ◽  
S. Lotfi-fard ◽  
P. Pillay
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Broken Rotor Bars

scholarly journals Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi-agent system approach using intelligent classifiers

2020 ◽  
Vol 14 (2) ◽  
pp. 245-255 ◽  
Author(s):  
Maria Drakaki ◽  
Yannis L. Karnavas ◽  
Athanasios D. Karlis ◽  
Ioannis D. Chasiotis ◽  
Panagiotis Tzionas
Keyword(s):  
Fault Diagnosis ◽  
System Approach ◽  
Induction Motors ◽  
Multi Agent System ◽  
Agent System ◽  
Broken Rotor Bars ◽  
Squirrel Cage ◽  
Multi Agent

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.

Sign in / Sign up

Export Citation Format

Share Document