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.
Train speed estimation using low-cost GPS receivers
Railroads use train speed measurements to assess operational efficiency and safety. The recent availability of low-cost GPS receivers presents an opportunity for massive cost reduction in monitoring continuously the speed and position of equipment across the entire network. GPS receivers estimate speed from geospatial position updates. However, low-cost GPS receivers can produce relatively large errors in position updates, thereby producing similar errors in speed estimates. Studies tend to focus on characterizing GPS receiver errors in urban road settings. Subsequently, railroads know very little about the nature of GPS errors along rural train routes. Smartphones nowadays have all the necessary sensor capabilities needed to test and validate a low-cost speed monitoring system. This study characterizes speed errors by using multiple smartphones onboard a hi-rail vehicle. The authors describe the data collected, the data processing algorithm developed to estimate speed, and the error quantification by comparing speed estimates to vehicle speedometer measurements reported by the hi-rail vehicle operators.
Simulation and control of hybrid renewable energy system connected to the grid
This paper presents a control and simulation of hybrid renewable energy system connected to the grid. The studied system includes a photovoltaic panel, synchronous permanent magnet generator based wind turbine, and a battery for storage energy. The PV array and wind systems are connected to the common DC bus by a boost converter. The battery is connected by a bi-directional DC/DC converter, and then integrated into the AC utility grid via a common DC/AC inverter. In order to extract the maximum energy, a simulation study has been carried out according to the meteorological conditions (wind speed and variable solar irradiance) while maintaining power quality at a satisfactory level. In order to capture the maximum power, a MPPT algorithm is applied for both wind turbine and photovoltaic panel. The modeling and simulation of the whole system has been performed under Matlab/Simulink environment. The obtained results show that the current and the voltage of grid side are sinusoidal and alternative forms, and the power injected to the grid is around the power produced by the hybrid system.