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

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.

scholarly journals Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

2006 ◽  
Vol 2006 ◽  
pp. 1-13 ◽  
Author(s):  
Tian Han ◽  
Bo-Suk Yang ◽  
Won-Ho Choi ◽  
Jae-Sik Kim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Measurement Data ◽  
Discrete Wavelet ◽  
Diagnosis System ◽  
Stator Current ◽  
Fault Diagnosis System

This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT), feature extraction, genetic algorithm (GA), and neural network (ANN) techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

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

scholarly journals Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1212 ◽  
Author(s):  
Yu-Min Hsueh ◽  
Veeresh Ramesh Ittangihal ◽  
Wei-Bin Wu ◽  
Hong-Chan Chang ◽  
Cheng-Chien Kuo
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Electrical Current ◽  
Current Signal ◽  
Operating Condition ◽  
Three Phase ◽  
Empirical Wavelet Transform ◽  
Recognition Technique ◽  
Grayscale Images

Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.

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