scholarly journals Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

2019 ◽  
Vol 9 (4) ◽  
pp. 616 ◽  
Author(s):  
Maciej Skowron ◽  
Marcin Wolkiewicz ◽  
Teresa Orlowska-Kowalska ◽  
Czeslaw Kowalski
Keyword(s):  
Neural Networks ◽  
Induction Motors ◽  
Processing System ◽  
Experimental Tests ◽  
Fault Classification ◽  
Stator Current ◽  
Short Circuits ◽  
Rotor Winding ◽  
Torque Values ◽  
Self Organizing

Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

scholarly journals Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors

2018 ◽  
Vol 3 (3) ◽  
pp. 106-116
Author(s):  
Saddam BENSAOUCHA ◽  
Sid Ahmed BESSEDIK ◽  
Aissa AMEUR ◽  
Abdellatif SEGHIOUR
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Induction Motors ◽  
Current Signal ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
The Neural Networks ◽  
Modulus Spectrum ◽  
Load Conditions

In this paper, a study has presented the performance of a neural networks technique to detect the broken rotor bars (BRBs) fault in induction motors (IMs). In this context, the fast Fourier transform (FFT) applied on Hilbert modulus obtained via the stator current signal has been used as a diagnostic signal to replace the FFT classic, the characteristics frequency are selected from the Hilbert modulus spectrum, in addition, the different load conditions are used as three inputs data for the neural networks. The efficiency of the proposed method is verified by simulation in MATLAB environment.

scholarly journals The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 3009
Author(s):  
Pawel Ewert
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Induction Motor ◽  
Induction Motors ◽  
Frequency Converter ◽  
Experimental Tests ◽  
Test Mass ◽  
Mechanical Vibrations ◽  
Stator Current

This article presents the effectiveness of bispectrum analysis for the detection of the rotor unbalance of an induction motor supplied by the mains and a frequency converter. Two diagnostic signals were analyzed, as well as the stator current and mechanical vibrations of the tested motors. The experimental tests were realized for two low-power induction motors, with one and two pole pairs, respectively. The unbalance was modeled using a test mass mounted on a specially prepared disc and directly on the rotor and the influence of this unbalance location was tested and discussed. The results of the bispectrum analysis are compared with results of Fourier transform and the effectiveness of unbalance detection are discussed and compared. The influence of the registration time of the analyzed signal on the quality of fault symptom analyses using both transforms was also tested. It is shown that the bispectrum analysis provides an increased number of fault symptoms in comparison with the classical spectral analysis as well as it is not sensitive to a shorter registration time of the diagnostic signals.

scholarly journals Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangjin Song ◽  
Jingtao Hu ◽  
Hongyu Zhu ◽  
Jilong Zhang
Keyword(s):  
Induction Motor ◽  
Analytical Approach ◽  
Induction Motors ◽  
Experimental Tests ◽  
Fault Models ◽  
Popular Method ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Stator Current ◽  
Theoretical Inference

Bearing faults are the most frequent faults of induction motors. The current spectrum analysis is an easy and popular method for the monitoring and detection of bearing faults. After a presentation of the existing fault models, this paper illustrates an analytical approach to evaluate the effects of the slot harmonics on the stator current in an induction motor with bearing fault. Simple and clear theoretical analysis results in some new current characteristic frequencies. Experimental tests with artificial bearing outer raceway fault are carried out by measuring stator current signals. The experimental results by spectral analysis of the stator current agree well with the theoretical inference.

scholarly journals Broken rotor bars fault detection in induction motors based on current envelope and neural network

2021 ◽  
Vol 3 (2 (111)) ◽  
pp. 88-95
Author(s):  
Mohammed Obaid Mustafa
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Induction Motors ◽  
Fault Classification ◽  
Monitoring Method ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
Motor Current ◽  
Machine Failure ◽  
Feature Extraction And Selection

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

scholarly journals Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8523
Author(s):  
Marcin Tomczyk ◽  
Ryszard Mielnik ◽  
Anna Plichta ◽  
Iwona Gołdasz ◽  
Maciej Sułowicz
Keyword(s):  
Genetic Algorithm ◽  
Induction Motor ◽  
Diagnostic Method ◽  
Induction Motors ◽  
Short Circuit ◽  
Experimental Tests ◽  
Stator Winding ◽  
Short Circuits ◽  
Cage Induction Motor ◽  
Industry Conditions

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

scholarly journals Assessment of failure to the stator winding of the induction motor by means of deep neural network

2019 ◽  
Vol 28 ◽  
pp. 01049
Author(s):  
Sebastian Kuroczycki ◽  
Konrad Górny ◽  
Wojciech Pietrowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Induction Motors ◽  
Induction Machine ◽  
Stator Winding ◽  
Continuous Wavelet ◽  
Common Causes ◽  
Short Circuits ◽  
Artificial Neural

Due to the fact that inter-turn short-circuits are the ones of the most common causes of damage to stator of induction motors, research on their early detection is still gaining in importance. The scientific novelty in the presented article is an approach in which a decision element informing about the failure of stator of induction machine is a deep artificial neural network. In the learning process, torque waveforms subjected to a continuous wavelet transform were used. In order to classify of the stator winding failures the accelerator of artificial neural networks was used.

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