Abstract- This paper uses data fusion based on fuzzy measure and fuzzy integral theory for stator winding inter-turn short circuit fault diagnosis in induction motors. Data fusion be considered in two level: feature level and decision level. Three-phase current signals of induction motor are used for fault diagnosis. Time-domain features are extracted from current signals, and a technique based on fuzzy density 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 using Sugeno fuzzy integral data fusion and at decision-level using Choquet 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.
Jafari,H. and Poshtan,J. (2016). Stator winding short-circuit fault diagnosis based on multi-sensor fuzzy data fusion. The Modares Journal of Electrical Engineering, 15(4), 27-34.
MLA
Jafari,H. , and Poshtan,J. . "Stator winding short-circuit fault diagnosis based on multi-sensor fuzzy data fusion", The Modares Journal of Electrical Engineering, 15, 4, 2016, 27-34.
HARVARD
Jafari,H.,Poshtan,J. (2016). 'Stator winding short-circuit fault diagnosis based on multi-sensor fuzzy data fusion', The Modares Journal of Electrical Engineering, 15(4), pp. 27-34.
CHICAGO
H. Jafari and J. Poshtan, "Stator winding short-circuit fault diagnosis based on multi-sensor fuzzy data fusion," The Modares Journal of Electrical Engineering, 15 4 (2016): 27-34,
VANCOUVER
Jafari,H.,Poshtan,J. Stator winding short-circuit fault diagnosis based on multi-sensor fuzzy data fusion. The Modares Journal of Electrical Engineering, 2016; 15(4): 27-34.
The Modares Journal of Electrical Engineering