모터의 전류치를 활용한 전동차 출입문의 특징추출 및 고장진단 예지 알고리즘 선정 방안 연구 (A study on the Feature extraction of the Rolling Stock door using the current value of the motor and the selection of a failure diagnosis prediction algorithm)

서지정보

· 발행기관 : 대한전기학회
· 수록지 정보 : 전기학회논문지 / 70권 / 1호 / 96 ~ 101페이지
· 저자명 : 임종국, 윤희중

초록

Recently, the manufacturing requirements of Rolling Stock are required to diagnose and manage the condition of major safety devices (doors, brake, signals, etc.) to suggest a plan for efficient maintenance. Based on these backgrounds and technology trends, research on Condition-Based Maintenance (CBM) and Prognostic Health Management (PHM) has been actively conducted in recent years. In this study, the current value of the engine drive motor of the door of a Rolling Stock is measured by dividing it into four classes (normal open/abnormal open, normally closed/abnormally closed), and statistically analyzed 13 factors of time domain statistics based on time domain statistics. After verifying the significance, suitable Features were extracted. Based on the machine learning theory, a predictive algorithm that can classify the extracted Features was selected, and the accuracy was verified against the actual measured data class with the selected prognosis algorithm.

영어초록

Recently, the manufacturing requirements of Rolling Stock are required to diagnose and manage the condition of major safety devices (doors, brake, signals, etc.) to suggest a plan for efficient maintenance. Based on these backgrounds and technology trends, research on Condition-Based Maintenance (CBM) and Prognostic Health Management (PHM) has been actively conducted in recent years. In this study, the current value of the engine drive motor of the door of a Rolling Stock is measured by dividing it into four classes (normal open/abnormal open, normally closed/abnormally closed), and statistically analyzed 13 factors of time domain statistics based on time domain statistics. After verifying the significance, suitable Features were extracted. Based on the machine learning theory, a predictive algorithm that can classify the extracted Features was selected, and the accuracy was verified against the actual measured data class with the selected prognosis algorithm.

참고자료

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