전자공학회논문지SC (Journal of the Institute of Electronics Engineers of Korea SC)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

신경회로망기반 다중고장모델에 의한 비선형시스템의 고장감지와 분류

(Fault Detection and Isolation of the Nonlinear systems Using Neural Network-Based Multi-Fault Models)

  • 이인수 (상주대학교 전자전기공학부)
  • Lee, In-Su (Dept.of Electronics Electric Engineering, Sangmyung University)
  • 발행 : 2002.01.01
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초록

본 논문에서는 비선형시스템에서 발생한 고장을 감지하고 분류하기 위한 신경회로망기반 다중고장모델을 이용한 고장감지 및 분류 방법을 제안한다. 시스템에 변화가 발생하면 시스템의 출력과 신경회로망 공칭모델 출력 사이의 오차가 고장감지를 위한 문턱값을 넘고, 고장이 감지되면 각 신경회로망 고장모델 출력과 시스템 출력 사이의 오차를 이용하여 통계적 기법으로 고장을 분류한다. 컴퓨터 시뮬레이션 결과로부터 제안한 고장진단방법이 비선형시스템에서의 고장감지 및 분류문제에 잘 적용됨을 알 수 있다.

In this paper, we propose an FDI(fault detection and isolation) method using neural network-based multi-fault models to detect and isolate faults in nonlinear systems. When a change in the system occurs, the errors between the system output and the neural network nominal system output cross a threshold, and once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

키워드

참고문헌

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