한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제14권1호
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  • Pages.1-8
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  • 2004
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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가스모니터링 시스템에서의 신경회로망 기반 센서고장진단

Neural Network-Based Sensor Fault Diagnosis in the Gas Monitoring System

  • 이인수 (상주대학교 전자전기공학부) ;
  • 조정환 (경북대학교 전자ㆍ전기ㆍ컴퓨터공학부) ;
  • 심창현 (㈜센스엔센서) ;
  • 이덕동 (경북대학교 전자ㆍ전기ㆍ컴퓨터공학부) ;
  • 전기준 (경북대학교 전자ㆍ전기ㆍ컴퓨터공학부)
  • 발행 : 2004.02.01
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초록

본 논문에서는 실내대기 가스모니터링 시스템에서의 센서 고장 진단을 위한 신경회로망 기반 고장진단방법을 제안한다. 제안한 고장진단 방법에서는 신호패턴추출을 위해 센서히터 온도조절방법을 이용하였으며, 분류를 위해서는 ART2 신경회로망을 이용하였다. 그리고 가스모니터링 시스템의 실제 데이터를 이용한 시뮬레이션을 통해 제안한 ART2 신경회로망 기반 센서고장진단방법의 성능과 유용성을 확인하였다.

In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

키워드

참고문헌

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