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Predicting vertical acceleration of railway wagons using regression algorithms

journal contribution
posted on 06.12.2017, 00:00 by G Shafiullah, A B M Shawkat Ali, Adam Thompson, Peter Wolfs
The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques’ performance has been measured using a set of attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

11

Issue

2

Start Page

290

End Page

299

Number of Pages

10

eISSN

1524-9050

ISSN

1524-9050

Location

USA

Publisher

IEEE

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Curtin University of Technology; Faculty of Arts, Business, Informatics and Education; Faculty of Sciences, Engineering and Health; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Journal

IEEE transactions on intelligent transportation systems.

Usage metrics

CQUniversity

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