Proceedings ArticleDOI
Identification of three phase induction motor incipient faults using neural network
A. Siddique,G.S. Yadava,Bhim Singh +2 more
- pp 30-33
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TLDR
In this paper, the applicability/feasibility of artificial neural network (ANN) technique for the detection and identification of incipient faults in an induction motor has been explored, where the authors used the graphical user interface (GUI) of neural network tool box under Matlab environment.Abstract:
The induction motors are most widely used motors in industrial, commercial and residential sectors because of enormous merits of these over other types of available electrical motors. These motors work under various operating stresses, which deteriorate their motor conditions giving rise to faults. The early detection of these deteriorating conditions in incipient phase and its removal/correction is very necessary for the prevention of any external faults/failure of induction motors reducing repair costs and motor outage time. Fault detection using analytical methods is not always possible because it requires a perfect knowledge of the motor model. The artificial neural network techniques are rather easy to develop and to perform. These networks can be applied when the information about the system is obtained from measurements, which later can be used in the training procedures of the neural networks. Neural detectors can be designed from simulation or experimental tests. In the present paper the applicability/feasibility of artificial neural network (ANN) technique for the detection and identification of incipient faults in an induction motor has been explored. Radial basis function (exact fit) approach has been used for ANN training and test. The applicability of the graphical user interface (GUI) of neural network tool box under Matlab environment has been explored in this paper. The various types of faults have been considered. Three phase instantaneous voltages and currents are utilized in proposed approach. Simulated fault current and voltage data have been used for testing of trained network.read more
Citations
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Dissertation
Health Monitoring System for Three Phase Induction Motor using Soft Computing Techniques
TL;DR: In this article, the authors propose a level-level-level approach to the problem of homonymity in homonym-level homonymization, i.e., homonym level.
Proceedings ArticleDOI
Soft computing technique for industrial drive failure identification using JavaNNS and Lab VIEW
TL;DR: In this article, the authors used feed forward neural network where the input data's are obtained from the positive and negative sequence component derived from hardware circuit to identify the stator fault and side band frequency of input motor current is obtained from Tektronics Power analyzer is used to identify rotor fault.
Journal Article
Fault Detection and Diagnosis of Broken Bar Problem in Induction Motors Base Wavelet Analysis and EMD Method: Case Study of Mobarakeh Steel Company in Iran
M. Ahmadi,M. Kafil,H. Ebrahimi +2 more
TL;DR: These signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors and it is found that, in the broken bar condition, the amount of CF factor is greater than the healthy condition.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book
Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines
TL;DR: In this article, the effects of time harmonics on various space-phasor loci, harmonic amplitude estimation Monitoring of the rotor speed and the rotor angle Monitoring various machine parameters Diagnosis, condition monitoring Bibliography Index
Proceedings ArticleDOI
Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors
Austin H. Bonnett,G.C. Soukup +1 more
TL;DR: In this article, the authors attempt to identify the various causes of stator and rotor failures in three-phase squirrel cage induction motors, and a specific methodology is proposed to facilitate an accurate analysis of these failures.
Proceedings ArticleDOI
An unsupervised, on-line system for induction motor fault detection using stator current monitoring
TL;DR: In this article, a new method for online induction motor fault detection is presented, which utilizes artificial neural networks to learn the spectral characteristics of a good motor operating online, which may contain many harmonics due to the load which correspond to normal operating conditions.
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