Proceedings ArticleDOI
Detection of stator winding fault in induction motors using a motor square current signature analysis (MSCSA)
V. Fernao Pires,Daniel Foito,João Martins,A. J. Pires +3 more
- pp 507-512
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TLDR
The motor square current signature analysis (MSCSA) is proposed, which uses the results of spectral analysis of the instantaneous square stator current to analyse the short-circuit fault inter-turn on an induction motor.Abstract:
In this paper the short-circuit fault inter-turn on the stator of an induction motor is analysed by an online diagnostic method. For the diagnostic method it is proposed the motor square current signature analysis (MSCSA). This method uses the results of spectral analysis of the instantaneous square stator current. The effects of stator square current spectrum are described and the related frequencies determined. This method is similar to the instantaneous power signature analysis, however has the advantage of just require one current sensor. Several simulation and experimental results are presented in order to illustrate the characteristics of the proposed method.read more
Citations
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Proceedings ArticleDOI
Detection of Broken Rotor Bars in Induction Machines using Machine Learning Methods
TL;DR: In this article, the authors applied a combination of classical fault detection approaches with different fault classification algorithms to reliably detect induction machine faults over a wide operating range and achieved an accuracy of 97.4%.
Journal ArticleDOI
Method for Identifying Stator and Rotor Faults of Induction Motors Based on Machine Vision
TL;DR: The proposed method for identifying stator and rotor faults of induction motors based on machine vision has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.
Journal ArticleDOI
Detection of Broken Rotor Bars in Induction Machines using Machine Learning Methods
TL;DR: This work applies a combination of classical fault detection approaches with different fault classification algorithms to reliably detect induction machine faults over a wide operating range and shows promising fault detection and classification capabilities.
References
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Journal ArticleDOI
Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review
TL;DR: A review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.
Journal ArticleDOI
A review of induction motors signature analysis as a medium for faults detection
TL;DR: The fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors are introduced.
Journal ArticleDOI
Online Diagnosis of Induction Motors Using MCSA
TL;DR: An online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed, able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor.
Journal ArticleDOI
Recent developments of induction motor drives fault diagnosis using AI techniques
TL;DR: A review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI) covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques.
Journal ArticleDOI
What stator current processing-based technique to use for induction motor rotor faults diagnosis?
Mohamed Benbouzid,G. B. Kliman +1 more
TL;DR: In this article, a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors is presented, which are then analyzed and compared to deduce the most appropriate technique for induction motor rotor rotor fault detection.
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