Principal components analysis and track quality index: A machine learning approach
| 项目名称： | Principal components analysis and track quality index: A machine learning approach |
| 摘要： | Track geometry data is often combined into a single parameter index referred to as a Track Quality Index or TQI. TQIs exhibit classical big data attributes: value, volume, velocity, veracity and variety and are used to obtain average-based assessment of track segments and schedule track maintenance. Using track geometry data from a sample mile track, this activity examines how to combine track geometry parameters into a low dimensional form (TQI) that simplifies the track properties without losing much variability in the data. This led to a principal component analysis approach, with a two-phase approach used to validate the use of principal components. First phase was to identify a classic machine learning technique that works well with track geometry data. The second step was to train the identified machine learning technique on the sample mile-track data using combined TQIs and principal components as defect predictors. The performance of the predictors were compared using true and false positive rates. The results show that three principal components were better at predicting defects and revealing salient characteristics in track geometry data than combined TQIs even though there were some correlations that are potentially useful for track maintenance. . |
| 状态： | Active |
| 资金： | 50000 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 管理组织： | University of Delaware, Newark |
| 项目负责人： | Zarembski, Allan |
| 执行机构： | University of Delaware, Newark |
| 主要研究人员： | Attoh-Okine, N O |
| 开始时间： | 20170601 |
| 预计完成日期： | 20200531 |
| 实际结束时间： | 0 |
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