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ABSTRACTThis paper proposes a new hypothesis for the formation process of short pitch rail corrugation. An FE wheel-track dynamic model is utilized to verify the hypothesis by reproducing corrugation initiation and consistent growth. It is found longitudinal compression modes are responsible for corrugation initiation with necessary initial excitation that allows flexibility for longitudinal vibration. Consistency between longitudinal compression and vertical bending eigenfrequencies of the wheel-track system is required for consistent corrugation growth, which also determines maximum corrugation amplitude. Corrugation initiates by frequency selection instead of wavelength fixing. The proposed mechanism can explain field observations including the wavelength and periodicity of corrugation in the Netherlands, why corrugation forms on continuously-supported tracks where pinned-pinned resonance does not exist, and the small variation between the corrugation wavelength and train speed.

Maintenance of generation units is a measure to ensure the reliability of power systems. In this paper, a novel blockchain-based truthful condition-based maintenance of generation units (T-CBMGU) platform is proposed to innovate and upgrade state-of-the-art CBMGU. In addition, two valid inequalities are proposed to accelerate the convergence speed of Benders decomposition in maintenance scheduling process. The proposed valid inequalities are formulated based on technical/physical analysis and greedy-based heuristic initialization. More specifically, for data acquisition and failure rate diagnosis/prognosis processes, T-CBMGU can ensure the immutability of the collected operational data. In this way, the influence of tampered data on the diagnosis/prognosis results in state-of-the-art CBMGU can be reduced. For maintenance scheduling and bidding to change scheduled time slot processes, in state-of-the-art CBMGU, the decision makers, i.e., independent system operators (ISOs), may not be trusted. However, in T-CBMGU, the scheduling and bidding processes are implemented automatically via smart contracts rather than by the ISOs; as such, incentives to manipulate data can be avoided. Finally, regarding performance of maintenance actions, in contrast to state-of-the-art CBMGU, the implementation process can be truthfully recorded by the T-CBMGU platform, which facilitates backtracking of responsibility. Then, the T-CBMGU platform and the valid inequalities are tested for the IEEE 300-bus power system. Furthermore, cases with tampered data and distrust caused by fairness manipulation are simulated to show the importance of using T-CBMGU. Finally, the Benders decomposition algorithm with valid inequalities is compared with other solvers to demonstrate its fast convergence speed.

Hydrogen fuel cell multiple unit vehicles are acquiring a central role in the transition process towards carbon neutral trains operation in non-electrified regional railway networks. In addition to their primary role as a transport mean, these vehicles offer significant potential for applications in innovative concepts such as smart grids. Compared to the pure electric propulsion systems, fuel cell technology allows for cogeneration processes by recovering generated heat in addition to the provision of the electrical power. This paper presents the analysis of fuel cell hybrid-electric multiple unit vehicle employed in regional railway transport during regular service, and in vehicle-to-grid application during the off-service hours, providing the electrical and thermal energy for stationary consumers in terminal stations. The system dynamics are modelled using a backward-looking quasi-static simulation approach, with implemented real-time optimization-based control strategy for managing the power flows between different components. In a case study of selected vehicle and railway services in the Netherlands, the fuel cell system showed average hydrogen consumption of 0.4 kg/km, with the overall electrical efficiency of 38.89%. In vehicle-to-grid scenario, the system satisfied complete stationary power demand, and provided about 327 kWh of thermal energy during 2-h operation, reaching the overall cogeneration efficiency of 66.81%.

The goal of this paper is to evaluate from a multi-objective perspective the performance on the detection of catenary support components when using state-of-the-art deep convolutional neural networks (DCNNs). The detection of components is the first step towards a complete automatized monitoring system that will provide actual information about defects in the catenary support devices. A series of experiments in an unified test environment for detection of components are performed using Faster-CNN, R-FCN, SSD, and YOLOv2. Through the comparison of different assessment indicators, such as precision, recall, average precision and mean average precision, the detection performance of the different DCNNs methods for the components of the catenary support devices is analyzed, discussed and evaluated. The experiment results show that among all considered methods, R-FCN is the more suitable for the detection of catenary support components.

En la angiopatía cerebral de moyamoya (AMM) existe una estenosis progresiva de la arteria carótida interna y/o de sus ramas principales y colaterales «moyamoya».

This paper develops a defect-based risk analysis methodology for estimating rail failure risk. The methodology relies on an evolution model addressing the severity level of rail surface defect, called squat. The risk of rail failure is assessed by analyzing squat failure probability using a probabilistic analysis of the squat cracks. For this purpose, a Bayesian inference method is employed to capture a robust model of squat failure probability when the squat becomes severe. Moreover, an experimental correlation between squat visual length and squat crack depth is obtained in order to define four severity categories. Relying on the failure probability and the severity categories of the squats, risk of future failure is categorized in three different scenarios (optimistic, average and pessimistic). To show the practicality and efficiency of the proposed methodology, a real example is illustrated.

Rail defect detection by video cameras has recently gained much attention in both academia and industry. Rail image data has two properties. It is highly imbalanced towards the non-defective class and it has a large number of unlabeled data samples available for semi-supervised learning techniques. In this paper we investigate if positive defective candidates selected from the unlabeled data can help improve the balance between the two classes and gain performance on detecting a specific type of defects called Squats. We compare data sampling techniques as well and conclude that the semi-supervised techniques are a reasonable alternative for improving performance on applications such as rail track Squat detection from image data.

Las intervenciones realizadas por los equipos de cuidados paliativos tienen como objetivo disminuir el sufrimiento físico y psicosocial de los pacientes y sus familias.

This paper presents a study of the parameters that influence the detection of squats when using axle box acceleration (ABA) measurements. The analysed parameters include the train speed, the location of the squat in the track relative to the sleeper, and the track design. The study is conducted relying on a validated finite element model. The ABA measurements are found to be highly influenced by train speed. To model this influence, a practical method is proposed to represent the relationship between the train speed and relevant characteristics of ABA (magnitude and frequency) at a given squat. Such practical relationships provide the opportunity to map the ABA signals when it proves impossible to run measuring trains at a constant speed (as is the case with any in-service railway vehicle). The parameter study also indicates that the major frequency characteristics of ABA at squats are strongly related to the natural frequencies of the track. This conclusion is validated by performing hammer tests on the railway track. It is suggested that a proper characterization of the tracks will lead to a better understanding of the ABA signals. Finally, it is claimed that the results presented in this paper can be used as a guideline to calibrate or improve squat detection algorithms based on ABA measurements on a track.

Non-electrified regional railway lines with typically employed diesel-electric multiple units require alternative propulsion systems to meet increasingly strict emissions regulations. With the aim to identify an optimal alternative to conventional diesel traction, this paper presents a model-based assessment of hydrogen-powered propulsion systems with an internal combustion engine or fuel cells as the prime mover, combined with different energy storage system configurations, based on lithium-ion batteries and/or double-layer capacitors. The analysis encompasses technology identification, design, modelling and assessment of alternative powertrains, explicitly considering case-related constraints imposed by the infrastructure, technical and operational requirements. Using a regional railway network in the Netherlands as a case, we investigate the possibilities in converting a conventional benchmark vehicle and provide the railway undertaking and decision-makers with valuable input for planning of future rolling stock investments. The results indicate the highest fuel-saving potential for fuel cell-based hybrid propulsion systems with lithium-ion battery or a hybrid energy storage system that combines both energy storage system technologies. The two configurations also demonstrate the highest reduction of greenhouse gas emissions compared to the benchmark diesel-driven vehicle, by about 25% for hydrogen produced by steam methane reforming, and about 19% for hydrogen obtained from electrolysis of water with grey electricity.

In this paper a new identification method for non-linear hybrid systems that have mixed continuous and discrete states by using fuzzy clustering and principal component analysis is described. The method first determines the hybrid characteristic of the system inspired by an inverse form of the merge method for clusters, which makes it possible to identify the unknown switching points of a process based on just input-output data. Using the switching points, a hard partition of the input-output space is obtained. Then, we propose to use Takagi-Sugeno (TS) fuzzy models with Gaussian MFs as sub-models for each partition. Thus, the overall model is hybrid-fuzzy and will include explicitly the hybrid behavior of the system (the detected switching points) by means of binary MFs, and in each partition all the other non-linearities by means of TS sub-models. An illustrative experiment on a hybrid-tank system is conducted to present the benefits of the proposed approach.