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.
Track Geometry Estimation from Car-Body Motions of Railway Vehicle
To enhance the safety and reliability of railway transportation, it is one of the most important tasks to check the track condition frequently and accurately. This paper describes a track condition monitoring technique using car-body motions. In an inverse problem to estimate track geometry in longitudinal level from vertical car-body acceleration and pitching rate measured by in-service vehicle, dynamic programming filter and Kalman filter were applied. Study results confirmed that proposed solutions can be used to estimate track geometry in level with desired precision.
A quantitative measure of nitrifying bacterial growth.
Nitrifying bacteria convert ammonia (NH3) to nitrate (NO3-) in a nitrification reaction. Methods to quantitatively separate the growth rate of these important bacterial populations from that of the dominant heterotrophic bacteria are important to our understanding of the nitrification process. The changing concentration of ammonia is often used as an indirect measure of nitrification but ammonification processes generate ammonia and confound this approach while heterotrophs remove nitrate via denitrification. Molecular probe methods can tell us what proportion of the microbial community is nitrifying bacteria but not their growth rate. The technique proposed here was able to quantify the growth rate of the nitrifying bacterial populations amidst complex ecological processes. The method incubates [methyl-3H] thymidine with water samples in the presence and absence of an inhibitor of nitrification-thiourea. The radioactively labeled DNA in the growing bacteria was extracted. The rate of incorporation of the label into the dividing bacterial DNA was used to determine bacterial growth rate. Total bacterial community growth rates in full-scale and pilot-scale fixed-film nitrifying reactors and an activated sludge reactor were 2.1 x 10(8), 4.1 x 10(8) and 0.4 x 10(8)cell ml(-1)d(-1), respectively; the growth rate of autotrophic-nitrifying bacteria was 0.7 x 10(8), 2.6 x 10(8) and 0.01 x 10(8)cell ml(-1)d(-1), respectively. Autotrophic-nitrifying bacteria contributed 30% and 60% of the total bacterial community growth rate in the nitrifying reactors whereas only 2% was observed in the activated sludge reactor that was not designed to nitrify. The rates of ammonia loss from the nitrifying reactors corresponded to the rate of growth of the nitrifying bacteria. This method has the potential to more often identify factors that enhance or limit nitrifying processes in both engineered and natural aquatic environments.