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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.12
- 수록면
- 353 - 361 (9page)
- DOI
- 10.33162/JAR.2019.12.19.4.353
이용수
이 논문의 연구 히스토리 (3)
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초록· 키워드
오류제보하기
Purpose: Passenger train door systems can undergo failure during operation, causing inconveniences such as service delays, breakdowns or even resulting in the human casualties. This study proposes a diagnostic process to predict such failure conditions so they can be avoided in advance.
Methods: A door test rig is prepared, in which the motor signals are acquired for different fault modes during the operation test. Various time domain features are extracted and selected using the Fisher Discriminant Ratio and Pearson correlation. Classifications are then performed by the k-Nearest Neighbor (k-NN) method for the door-open and door-close operations, respectively.
Results: Overall, the fault modes are identified quite well except for the artificial bearing wear. The result is worse in thedoor-close operation because the high peak current applied near the end to ensure the door closing may have affected the classification accuracy.
Conclusion: The proposed fault diagnosis method is easy to implement for the passenger train door system. Once the diagnosis system is trained via the seeded fault modes, it can be applied with accuracy to the fault diagnostics of train door systems, and be used for the efficient maintenance.
Methods: A door test rig is prepared, in which the motor signals are acquired for different fault modes during the operation test. Various time domain features are extracted and selected using the Fisher Discriminant Ratio and Pearson correlation. Classifications are then performed by the k-Nearest Neighbor (k-NN) method for the door-open and door-close operations, respectively.
Results: Overall, the fault modes are identified quite well except for the artificial bearing wear. The result is worse in thedoor-close operation because the high peak current applied near the end to ensure the door closing may have affected the classification accuracy.
Conclusion: The proposed fault diagnosis method is easy to implement for the passenger train door system. Once the diagnosis system is trained via the seeded fault modes, it can be applied with accuracy to the fault diagnostics of train door systems, and be used for the efficient maintenance.
목차
1. 서론
2. 데이터 계측
3. 특징신호 추출 기반 고장진단
4. 결론
References
참고문헌 (12)
참고문헌 신청
Bai, H. / 2010 / A generic fault detection and diagnosis approach for pneumatic and electric driven railway assets
Ham, S. J. / 2019 / Diagnosis of railway component based on motor current signal / The Korean Society of Mechanical Engineers Reliability Engineering Division 2019 Spring Conference :28
Turgis, F. / 2009 / Design of a testing bench for simulating tightened-up operating conditions of train’s passenger access / Proc. European Safety and Reliability Conference (ESREL) :21~23
Cauffriez, L. / 2013 / Robustness study and reliability growth based on exploratory design of experiments and statistical analysis : a case study using a train door test bench / The International Journal of Advanced Manufacturing Technology 66(1-4):27~44
Shuai, L. / 2014 / Research on urban rail train passenger door system fault diagnosis using PCA and rough set / Open Mechanical Engineering Journal 8:340~348
Ham, S. J. / 2019 / Diagnosis of railway component based on motor current signal / The Korean Society of Mechanical Engineers Reliability Engineering Division 2019 Spring Conference :28
Turgis, F. / 2009 / Design of a testing bench for simulating tightened-up operating conditions of train’s passenger access / Proc. European Safety and Reliability Conference (ESREL) :21~23
Cauffriez, L. / 2013 / Robustness study and reliability growth based on exploratory design of experiments and statistical analysis : a case study using a train door test bench / The International Journal of Advanced Manufacturing Technology 66(1-4):27~44
Shuai, L. / 2014 / Research on urban rail train passenger door system fault diagnosis using PCA and rough set / Open Mechanical Engineering Journal 8:340~348
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