지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록· 키워드
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This paper presents a method based on neural network to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic model of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situation or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transform and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been
tested with a different amount of load torque, and it is capable
of working on-line and of recognizing all normal and ill conditions.
tested with a different amount of load torque, and it is capable
of working on-line and of recognizing all normal and ill conditions.
목차
Abstract
1- Introduction
2- Modeling Induction motor, using winding function method
3- Current signal analysis method to detect broken rotor bars
4- Detection of rotor faults by Using neural network
5- Results of simulation
6- Practical results
7- Conclusion
Appendix A
Acknowledgment
References
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UCI(KEPA) : I410-ECN-0101-2009-560-014560563