Improved Railway Track Irregularities Classification by a Model Inversion Approach

René Schenkendorf, Jörn C. Groos, and Beate Dutschk
Submission Type: 
Full Paper
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phmec_16_016.pdf1.16 MBJuly 27, 2016 - 12:38am

Over time railway networks have become complex systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures are made up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual as well as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of kilometers of railway tracks are almost continuously monitored.
Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a simplified vehicle suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded system Responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound basis of subsequent condition monitoring analyses. In particular, a classification algorithm is implemented to identify a simulated railway track irregularity automatically.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
016
Page Count: 
8
Submission Keywords: 
Condition Based Maintenance
rail defect detection
railway systems
Hybrid Approach
Infrastructure Monitoring
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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