COMMON CROSSING CONDITION MONITORING WITH ON BOARD INERTIAL MEASUREMENTS

A railway turnout is an element of the railway infrastructure that influences the reliability of a railway traffic operation the most. The growing necessity for the reliability and availability in the railway transportation promotes a wide use of condition monitoring systems. These systems are typically based on the measurement of the dynamic response during operation. The inertial dynamic response measurement with on-board systems is the simplest and reliable way of monitoring the railway infrastructure. However, the new possibilities of condition monitoring are faced with new challenges of the measured information utilization. The paper deals with the condition monitoring of the most critical part of turnouts - the common crossing. The application of an on-board inertial measurement system ESAH-F for a crossing condition monitoring is presented and explained. The inertial measurements are characterized with the low correlation of maximal vertical accelerations to the lifetime. The data mining approach is used to recover the latent relations in the measurement’s information. An additional time domain and spectral feature sets are extracted from axle-box acceleration signals. The popular spectral kurtosis features are used additionally to the wavelet ones. The feature monotonicity ranking is carried out to select the most suited features for the condition indicator. The most significant features are fused in a one condition indicator with a principal component analysis. The proposed condition indicator delivers an almost two-time higher correlation to the lifetime as the maximal vertical accelerations. The regression analysis of the indicator to the lifetime with an exponential fit proves its good applicability for the crossing residual useful life prognosis

Issues on simulation of the railway rolling stock operation process – a system and literature review

Railway traffic simulation, taking into account operation and maintenance conditions, is not a new issue in the literature. External effects in such networks (eg. level crossings) were not taken into account in studies. The used models do not take into account sufficiently the process of degradation and recovery of the network. From the technical side, currently carried out simulations are made using similar approaches and techniques as in the initial stage of research. Well-established work in this area could be the basis for evaluation of new solutions. However, the progress in simulation tools during the last years, especially in performance and programming architecture, attempt to create a modern simulation tool. In the paper were presented the main assumptions for the evaluated event-based simulation method, with application to stiff-track transportation network

INDICATORS FOR COMMON CROSSING STRUCTURAL HEALTH MONITORING WITH TRACK-SIDE INERTIAL MEASUREMENTS

This paper focuses on the experimental study of an alteration in the railway crossing dynamic response due to the rolling surface degradation during a crossing’s lifecycle. The maximal acceleration measured with the track-side measurement system as well as the impact position monitoring show no significant statistical relation to the rolling surface degradation. The additional spectral features are extracted from the acceleration measurements with a wavelet transform to improve the information usage. The reliable prediction of the railway crossing remaining useful life (RUL) demands the trustworthy indicators of structural health that systematically change during the lifecycle. The popular simple machine learning methods like principal component analysis and partial least square regression are used to retrieve two indicators from the experimental information. The feature ranking and selection are used to remove the redundant information and increase the relation of indicators to the lifetime

Hybrid model for proactive dispatching of railway operation under the consideration of random disturbances in dynamic circumstances

With the increasing traffic demand and limited infrastructure expansion, railway networks are often operated close to the full capacity, especially in heavily used areas. As a result, the basic timetable is quite susceptible to the operational disturbances, and thereby the propagation and accumulation of delays significantly degrade the service level for customers. To solve this problem, extensive researches have been conducted by focusing on the predefined robust timetables and the real time dispatching algorithm development. However, it has been widely recognized that excessive robust timetables may deteriorate the operating capacity of the railway network and the addition of recovery time and buffer time can be hardly implemented in the congested area. Moreover, most of the conventional dispatching algorithms ignore the further potential random disturbances during the dispatching process, which yield non-implementable dispatching solutions and, as consequences, inferior punctuality and repetitive dispatching actions. To this end, this project aims to develop a new algorithm for real-world dispatching process with the consideration of risk-oriented random disturbances in dynamic circumstances. In the procedure of this project, an operational risk map will be firstly produced: by simulating considerable amount of disturbed timetables with random disturbances generated in a Monte-Carlo scheme and calculating the corresponding expected negative impacts (average total weighted waiting time among all the disturbances scenarios), different levels of operational risk will be assigned to each block section in the studied railway network. Within a rolling time horizon framework, conflicts are detected with the inclusion of risk-oriented random disturbances in each block section, and the near-optimal dispatching solutions are calculated by using Tabu search algorithm. Finally, three indicators including total weighted waiting time, the number of relative reordering and average absolute retiming, are chosen to testify the effectiveness and advantages of the proposed dispatching algorithm. The sensitivity analysis of dispatching-related parameters is conducted afterwards to investigate the most appropriate relevant parameters for the specific studied area. The proposed algorithms are expected to be capable of automatically producing near-optimal and robust dispatching solutions with sufficient punctuality achieved

Modelling of interactions between rail service and travel demand: a passenger-oriented analysis

The proposed research is situated in the field of design, management and optimisation in railway network operations. Rail transport has in its favour several specific features which make it a key factor in public transport management, above all in high-density contexts. Indeed, such a system is environmentally friendly (reduced pollutant emissions), high-performing (high travel speeds and low values of headways), competitive (low unitary costs per seat-km or carried passenger-km) and presents a high degree of adaptability to intermodality. However, it manifests high vulnerability in the case of breakdowns. This occurs because a faulty convoy cannot be easily overtaken and, sometimes, cannot be easily removed from the line, especially in the case of isolated systems (i.e. systems which are not integrated into an effective network) or when a breakdown occurs on open tracks. Thus, re-establishing ordinary operational conditions may require excessive amounts of time and, as a consequence, an inevitable increase in inconvenience (user generalised cost) for passengers, who might decide to abandon the system or, if already on board, to exclude the railway system from their choice set for the future. It follows that developing appropriate techniques and decision support tools for optimising rail system management, both in ordinary and disruption conditions, would consent a clear influence of the modal split in favour of public transport and, therefore, encourage an important reduction in the externalities caused by the use of private transport, such as air and noise pollution, traffic congestion and accidents, bringing clear benefits to the quality of life for both transport users and non-users (i.e. individuals who are not system users). Managing to model such a complex context, based on numerous interactions among the various components (i.e. infrastructure, signalling system, rolling stock and timetables) is no mean feat. Moreover, in many cases, a fundamental element, which is the inclusion of the modelling of travel demand features in the simulation of railway operations, is neglected. Railway transport, just as any other transport system, is not finalised to itself, but its task is to move people or goods around, and, therefore, a realistic and accurate cost-benefit analysis cannot ignore involved flows features. In particular, considering travel demand into the analysis framework presents a two-sided effect. Primarily, it leads to introduce elements such as convoy capacity constraints and the assessment of dwell times as flow-dependent factors which make the simulation as close as possible to the reality. Specifically, the former allows to take into account the eventuality that not all passengers can board the first arriving train, but only a part of them, due to overcrowded conditions, with a consequent increase in waiting times. Due consideration of this factor is fundamental because, if it were to be repeated, it would make a further contribution to passengers’ discontent. While, as regards the estimate of dwell times on the basis of flows, it becomes fundamental in the planning phase. In fact, estimating dwell times as fixed values, ideally equal for all runs and all stations, can induce differences between actual and planned operations, with a subsequent deterioration in system performance. Thus, neglecting these aspects, above all in crowded contexts, would render the simulation distorted, both in terms of costs and benefits. The second aspect, on the other hand, concerns the correct assessment of effects of the strategies put in place, both in planning phases (strategic decisions such as the realisation of a new infrastructure, the improvement of the current signalling system or the purchasing of new rolling stock) and in operational phases (operational decisions such as the definition of intervention strategies for addressing disruption conditions). In fact, in the management of failures, to date, there are operational procedures which are based on hypothetical times for re-establishing ordinary conditions, estimated by the train driver or by the staff of the operation centre, who, generally, tend to minimise the impact exclusively from the company’s point of view (minimisation of operational costs), rather than from the standpoint of passengers. Additionally, in the definition of intervention strategies, passenger flow and its variation in time (different temporal intervals) and space (different points in the railway network) are rarely considered. It appears obvious, therefore, how the proposed re-examination of the dispatching and rescheduling tasks in a passenger-orientated perspective, should be accompanied by the development of estimation and forecasting techniques for travel demand, aimed at correctly taking into account the peculiarities of the railway system; as well as by the generation of ad-hoc tools designed to simulate the behaviour of passengers in the various phases of the trip (turnstile access, transfer from the turnstiles to the platform, waiting on platform, boarding and alighting process, etc.). The latest workstream in this present study concerns the analysis of the energy problems associated to rail transport. This is closely linked to what has so far been described. Indeed, in order to implement proper energy saving policies, it is, above all, necessary to obtain a reliable estimate of the involved operational times (recovery times, inversion times, buffer times, etc.). Moreover, as the adoption of eco-driving strategies generates an increase in passenger travel times, with everything that this involves, it is important to investigate the trade-off between energy efficiency and increase in user generalised costs. Within this framework, the present study aims at providing a DSS (Decision Support System) for all phases of planning and management of rail transport systems, from that of timetabling to dispatching and rescheduling, also considering space-time travel demand variability as well as the definition of suitable energy-saving policies, by adopting a passenger-orientated perspective