RISS 학술연구정보서비스

검색
다국어 입력

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)
  • 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
  • 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
닫기
    인기검색어 순위 펼치기

    RISS 인기검색어

      KCI등재

      Locating Mechanical Damages Using Magnetic Flux Leakage Inspection in Gas Pipeline System

      한글로보기

      https://www.riss.kr/link?id=A104907373

      • 0

        상세조회
      • 0

        다운로드
      서지정보 열기
      • 내보내기
      • 내책장담기
      • 공유하기
      • 오류접수

      부가정보

      다국어 초록 (Multilingual Abstract)

      Gas transmission pipelines are often inspected and monitored using the magnetic flux leakage method. An inspection vehicle known as a “pig” is launched into the pipeline and conveyed along the pipe by the pressure of natural gas. The pig contains a magnetizer, an array of sensors and a microprocessor-based data acquisition system for logging data. This paper describes magnetic flux leakage (MFL) signal processing used for detecting mechanical damages during an in-line inspection. The overall approach employs noise removal and clustering technique. The proposed method is computationally efficient and can easily be implemented. Results are presented and verified by field tests from an application of the signal processing.
      번역하기

      Gas transmission pipelines are often inspected and monitored using the magnetic flux leakage method. An inspection vehicle known as a “pig” is launched into the pipeline and conveyed along the pipe by the pressure of natural gas. The pig contains ...

      Gas transmission pipelines are often inspected and monitored using the magnetic flux leakage method. An inspection vehicle known as a “pig” is launched into the pipeline and conveyed along the pipe by the pressure of natural gas. The pig contains a magnetizer, an array of sensors and a microprocessor-based data acquisition system for logging data. This paper describes magnetic flux leakage (MFL) signal processing used for detecting mechanical damages during an in-line inspection. The overall approach employs noise removal and clustering technique. The proposed method is computationally efficient and can easily be implemented. Results are presented and verified by field tests from an application of the signal processing.

      더보기

      참고문헌 (Reference)

      1 Clapham, L., "Understanding Magnetic Flux Leakage(MFL) Signals from Mechanical Damage in Pipelines" United States Department of Transportation 2008

      2 Tou, J. T., "Pattern Recognition Principles" Addison-Wesley Publishing Company 1974

      3 이진이, "Nondestructive testing and crack evaluation of ferromagnetic material by using the linearly integrated hall sensor array" 대한기계학회 22 (22): 2310-2317, 2008

      4 Weisweiler, F. J., "Non-Destructive Testing of Large-Diameter Pipe for Oil and Gas Transmission Lines" VCH 1987

      5 Ivanov, P. A., "Magnetic Flux Leakage Modeling for Mechanical Damage in Transmission Pipelines" 34 : 3020-3023, 1998

      6 Mandayam, S., "Invariance Transformations for Magnetic Flux Leakage Signals" 32 (32): 1577-1580, 1996

      7 Afzal, M., "Enhancement and Detection of Mechanical Damage MFL Signals from Gas Pipeline Inspection" 18A : 805-812, 1999

      8 Blitz, J., "Electrical and Magnetic Methods of Nondestructive Testing" Adam Hilger 1991

      9 Shapiro, L. G., "Computer Vision" Prentice Hall 2001

      10 Canny, John, "A Computational Approach to Edge Detection" 8 (8): 679-698, 1986

      1 Clapham, L., "Understanding Magnetic Flux Leakage(MFL) Signals from Mechanical Damage in Pipelines" United States Department of Transportation 2008

      2 Tou, J. T., "Pattern Recognition Principles" Addison-Wesley Publishing Company 1974

      3 이진이, "Nondestructive testing and crack evaluation of ferromagnetic material by using the linearly integrated hall sensor array" 대한기계학회 22 (22): 2310-2317, 2008

      4 Weisweiler, F. J., "Non-Destructive Testing of Large-Diameter Pipe for Oil and Gas Transmission Lines" VCH 1987

      5 Ivanov, P. A., "Magnetic Flux Leakage Modeling for Mechanical Damage in Transmission Pipelines" 34 : 3020-3023, 1998

      6 Mandayam, S., "Invariance Transformations for Magnetic Flux Leakage Signals" 32 (32): 1577-1580, 1996

      7 Afzal, M., "Enhancement and Detection of Mechanical Damage MFL Signals from Gas Pipeline Inspection" 18A : 805-812, 1999

      8 Blitz, J., "Electrical and Magnetic Methods of Nondestructive Testing" Adam Hilger 1991

      9 Shapiro, L. G., "Computer Vision" Prentice Hall 2001

      10 Canny, John, "A Computational Approach to Edge Detection" 8 (8): 679-698, 1986

      더보기

      분석정보

      View

      상세정보조회

      0

      Usage

      원문다운로드

      0

      대출신청

      0

      복사신청

      0

      EDDS신청

      0

      동일 주제 내 활용도 TOP

      더보기

      주제

      연도별 연구동향

      연도별 활용동향

      연관논문

      연구자 네트워크맵

      공동연구자 (7)

      유사연구자 (20) 활용도상위20명

      인용정보 인용지수 설명보기

      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2026 평가예정 재인증평가 신청대상 (재인증)
      2020-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2017-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2003-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2002-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2001-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
      더보기

      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.36 0.36 0.27
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.21 0.19 0.467 0.14
      더보기

      이 자료와 함께 이용한 RISS 자료

      나만을 위한 추천자료

      해외이동버튼