This work presents the results of a measurement campaign to demonstrate the effectiveness of the axle box acceleration (ABA) technology for detecting rail defects. The measurements were conducted along the Iron Ore line between Sweden and Norway for the IN2TRACK3 project. This line is mostly single-track with passenger-freight mixed traffic and heavy axle load. Historical data and track information data were not considered in this study. By analyzing data acquired from the accelerometers in vertical and longitudinal directions, rail defects were detected in near real-time using big-data analytics. For our validated sections, 100% of rail defects (including squats) were detected using time-frequency analysis and an outlier detection approach. The methodology also allows for identifying priority locations, e.g., defective welds, joints, transition zones, etc., and its use for prescriptive maintenance recommendations is being explored in the framework of the IAM4RAIL project.@en

Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones using acceleration measurements on multiple axle boxes (multi-ABA) of a passing train is presented. To showcase its capability, the measurements in the Netherlands, Sweden, and Norway are analyzed and dis-cussed. It is found that different bridges and transition zones exhibit unique char-acteristics including dominant wavelengths and energy distribution. Based on these unique characteristics, the geometry and support conditions at different lo-cations of a transition zone can be evaluated. Higher train speed makes the char-acteristics more pronounced. The results demonstrate that the multi-ABA meas-urement has the potential to evaluate and thus monitor the health conditions of various transition zones.@en

While various train-borne techniques have been developed for measuring railway track stiffness, differentiating stiffness at different track layers remains a challenge. This study proposes a digital twin framework for the vehicle–track interaction system, which enables track stiffness evaluations based on axle box accelerations (ABA). The digital twin consists of a physics-based model, a model library and data-driven models. Compared to existing techniques, the proposed method simultaneously evaluates the stiffness of the railpad, sleeper and ballast layers at a sleeper spacing resolution, while being robust to varying track conditions, such as track irregularities and vehicle speeds. This is accomplished by employing a localized frequency-domain ABA feature capable of distinguishing between the characteristics of different track layers. Furthermore, track stiffness is evaluated in near real-time. This is achieved using a model library derived from physics-based simulations of a range of track conditions. Two data-driven models that can quickly select or interpolate model instances contained in the library are developed. During operation, the data-driven models use the measured ABA features as input and then infer the stiffness for the different track layers. The proposed method is applied to evaluate the track stiffness of a downscale test rig in a case study. The track stiffness evaluated by the proposed method is compared with that obtained through hammer tests and with the observations of the track component conditions. These comparisons show that the proposed method can capture the stiffness variations due to periodically fastened clamps and substructure misalignments at different speeds. In addition, the proposed method is demonstrated to be superior to the commonly used hammer test method for evaluating track stiffness under loaded conditions.@en

The railway industry has the potential to make a strong contribution to the achievement of various sustainable development goals, by an expansion of its role in the transportation system of different countries. To realize this, complex technological and societal challenges are to be addressed, along with the development of suitable state-of-the-art methodologies fully tailored to the particular needs of the wide variety of railway infrastructure types and conditions. Artificial intelligence (AI) methods have been increasingly and successfully applied to solve practical problems in the railway infrastructure domain for over two decades. This paper proposes a review of the development of AI methods in railway infrastructure. First, we present a survey limited to selected journal papers published between 2010 and 2022. Bibliographical statistics are obtained, showing the increasing number of contributions in this field. Then, we select key AI methodologies and discuss their applications in the railway infrastructure. Next, AI methods for key railway components are analyzed. Finally, current challenges and future opportunities are discussed.

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