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Paper Information

Journal:   MODARES JOURNAL OF ELECTRICAL ENGINEERING   WINTER 2015 , Volume 15 , Number 4 ; Page(s) 27 To 34.
 
Paper: 

STATOR WINDING SHORT-CIRCUIT FAULT DIAGNOSIS BASED ON MULTI-SENSOR FUZZY DATA FUSION

 
 
Author(s):  JAFARI HAMIDEH, POSHTAN JAVAD
 
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Abstract: 

This paper uses data fusion based on fuzzy integral theory for stator winding inter-turn short circuit fault diagnosis in induction motors. Time-domain features are extracted from current signals, and a technique is proposed to choose appropriate features. The fuzzy c-mean analysis method is employed to classify different modes. It is used to choose the membership values of each feature for each fault mode. Finally, different features are fused at feature-level and decision-level using fuzzy integral data fusion to produce diagnostic results. Results show that fuzzy data fusion method performs very well for fault diagnosis in a 4hp laboratory induction motor.

 
Keyword(s): DATA FUSION, FAULT DIAGNOSIS, FUZZY INTEGRAL, STATOR THREE-PHASE CURRENT
 
 
References: 
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Click to Cite.
APA: Copy

JAFARI, H., & POSHTAN, J. (2015). STATOR WINDING SHORT-CIRCUIT FAULT DIAGNOSIS BASED ON MULTI-SENSOR FUZZY DATA FUSION. MODARES JOURNAL OF ELECTRICAL ENGINEERING, 15(4 ), 27-34. https://www.sid.ir/en/journal/ViewPaper.aspx?id=570965



Vancouver: Copy

JAFARI HAMIDEH, POSHTAN JAVAD. STATOR WINDING SHORT-CIRCUIT FAULT DIAGNOSIS BASED ON MULTI-SENSOR FUZZY DATA FUSION. MODARES JOURNAL OF ELECTRICAL ENGINEERING. 2015 [cited 2021May08];15(4 ):27-34. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=570965



IEEE: Copy

JAFARI, H., POSHTAN, J., 2015. STATOR WINDING SHORT-CIRCUIT FAULT DIAGNOSIS BASED ON MULTI-SENSOR FUZZY DATA FUSION. MODARES JOURNAL OF ELECTRICAL ENGINEERING, [online] 15(4 ), pp.27-34. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=570965.



 
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