NDLI: Multiple features extraction and selection for detection and classification of stator winding faults

Content Provider	IET Digital Library
Author	Haroun, Smail Seghir, Amirouche Nait Touati, Said
Abstract	In this study, a new effective approach for detection and classification of stator winding faults in induction motors is presented. The approach is based on current analysis. It uses multiple features extraction techniques, where Park transform, zero crossing time signal, and the envelope are extracted from the three-phase stator currents. Then, statistical features are calculated from time and frequency domains of each extracted signal. The Features selection techniques (ReliefF, minimum redundancy and max relevancy, and support vector machine approach based on recursive feature elimination) are used to select from the extracted features the most relevant ones. As a classifier, the self-organising map neural network is used. The proposed procedure is experimentally studied using stator current signals obtained from various faulty cases and a healthy induction motor at different load variations. The experimental results verify that the proposed strategy is able to distinguish the faulty cases from the healthy ones. Also, it effectively identifies the faulty phase in addition to the extent of the fault.
Starting Page	339
Ending Page	346
Page Count	8
ISSN	17518660
Volume Number	12
e-ISSN	17518679
Issue Number	Issue 3, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-epa/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-epa.2017.0457
Journal	IET Electric Power Applications
Publisher Date	2017-11-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Asynchronous Machine Extracted Signal Fault Diagnosis Feature Extraction Feature Selection Frequency Domain Induction Motors Integral Transforms in Numerical Analysis Knowledge Engineering Technique Multiple Feature Extraction Multiple Feature Selection Neural Computing Technique Park Transform Recursive Feature Elimination Self Organising Feature Maps Self-organising Map Neural Network Stator Current Signal Stator Winding Fault Classification Stator Winding Fault Detection Stators Support Vector Machine Three-phase Stator Current Time Domain Transform Zero Crossing Time Signal
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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