| Title |
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 |
| Authors |
임종국(Jong-Kook Lim) ; 윤희중(Hee-Jung Yoon) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.096 |
| Keywords |
Condition Based Maintenance; Machine Learning; Signal Processing; Dense KNN |
| Abstract |
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. |