| Title |
Fault Detection and Diagnosis Systems of Induction Machines using Real-Time Stochastic Modeling Approach |
| Authors |
이진우(Lee, Jin-Woo) ; 김광수(Kim, Kwang-Soo) ; 조현철(Cho, Hyun-Cheol) ; 이영진(Lee, Young-Jin) ; 이권순(Lee, Kwon-Soon) |
| Keywords |
Fault Detection ; Induction Motor ; Online Probability Density Estimation ; Maintenance System |
| Abstract |
This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis of the proposed estimation to demonstrate its convergence property by using statistical convergence and system stability theories. We apply our fault detection approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields. |