Improved Railway Track Irregularities Classification by a Model Inversion Approach

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 aswell 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 novel hybrid Approach is presented. To this end, 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 data-driven condition monitoring analyses. In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically

Analyzing uncertainties in model response using the point estimate method: applications from railway asset management

Predicting current and future states of rail infrastructure based on existing data and measurements is essential for optimal maintenance and operation of railway systems. Mathematical models are helpful tools for detecting failures and extrapolating current states into the future. This, however, inherently gives rise to uncertainties in the model response that must be analyzed carefully to avoid misleading results and conclusions. Commonly, Monte Carlo (MC) simulations are used for such analyses which often require a large number of sample points to be evaluated for convergence. Moreover, even if quite close to the exact distributions, the MC approach necessarily provides approximate results only. In contrast to that, the present contribution reviews an alternative way of computing important statistical quantities of the model response. The so-called point estimate method (PEM), which can be shown to be exact under certain constraints, usually (i.e., depending on the number of input variables) works with only a few specific sample points. Thus, the PEM helps to reduce the computational load for model evaluation considerably in the case of complex models or large-scale applications. To demonstrate the PEM, five academic but typical examples of railway asset management are analyzed in more detail: i) track degradation, ii) reliability analysis of composite systems, iii) terminal reliability in rail networks, iv) failure detection/identification using decision trees, and v) track condition modeling incorporating maintenance. Advantages as well as limitations of the PEM in comparison to common MC simulations are discussed

Deeper insight in railway switch condition nowcasting

Railway switches are a crucial part of the railway system but prone to failures. Nowadays a common approach to monitor switches with electrical engines is to measure the current consumption of the switch's engine. Since this measurement principle is valid for creating raw data, effort is put into improving the evaluation of the current graph for monitoring the condition of switches. This aims to improve data analysis techniques for a well-functioning and reliable railway system avoiding unnecessary train delays due to switch failures. In this work unsupervised data mining techniques are investigated as a basis for the prospective step to condition based prognostics with a deeper understanding of the characteristics of the current graph and their meaning. Therefore different features and various combinations are explored as well. Learning from historic data and gaining knowledge is essential for further improvement and is combined with domain knowledge. This work aims to detect anomalies and failures from the current graph to prospectively identify degradation, avoid potential false alerts and switch malfunctions. It also enables a step towards supporting maintenance with better and more efficient maintenance plans, more reliable and understandable switch condition monitoring and builds a fundament to predictive maintenance