Recognition of track defects through measured acceleration - part 2. (October 2019)

• Record Type: Journal Article
• Authors: Bahamon-Blanco, S; Rapp, S; Rupp, C; Liu, J; Martin, U
• Is Part Of: IOP conference series. Volume 615(2019)
• Publication Date: 2019-10
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/615/1/012122
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: For an optimized maintenance strategy, the early detection of track defects is necessary. Mounted sensors (e.g. acceleration sensors) on in-service trains are very suitable for track monitoring. With the continuous measurement of axle-box acceleration, short wavelength defects can be identified. For example, these defects can be rail breaks or cracks (i.e. rail defects), or local instabilities. Local instabilities can reduce the track quality in a short period of time. For an efficient data analysis of the acceleration signal and classification of different track defects, the development of appropriate methods is necessary. Therefore, a track-vehicle scale model was built to generate acceleration data for typical types of failures. With the generated dataset, developed algorithms for pattern recognition can be easily tested. In the second part of this research, three models created by the supervised learning method are trained and tested for the detection of the local instability in the vertical acceleration signal. The model A is trained with 78 laps and uses a manual classification. The chosen classifier for the model is a bagged tree algorithm implemented in the software MATLAB. The developed models distinguished between no failure, rail defect and local instability. For the training process of the model, the measured acceleration is treated statistically (e.g. Root-mean-square, Standard deviation, Spectral peaks and power). Subsequently test data for different scenarios is generated and used in the prediction model. With this model the track defects in the track-vehicle scale model are detected and classified very reliably. In contrast to existing methods, a machine learning approach is used for the non-destructive detection of the local instability. The results of the model are also improved by the model B and C by using 139 laps as training data, an automatic classification and an optimization of the statistics. The knowledge gained, can be used for acceleration data from in­service trains in regular operation, by adapting the developed model.