AZ
A. Zoeteman
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43 records found
1
Conference paper
(2025)
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Nikhil Manakshya, Xinxin Yu, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet, Arjen Zoeteman, Zili Li
The coefficient of friction (COF), defined as the maximum of the adhesion coefficient for a given contact condition, fluctuates rapidly due to environmental and operational factors. This paper introduces a torque modulation-based method for COF estimation. A simplified analytical model of the Manchester benchmark bogie operating under dry adhesion conditions is used to evaluate this method. The study presents an analytical equation that confirms earlier simulation-based findings showing a phase difference between applied torque modulation and resulting motor angular velocity. This phase relationship is shown to reflect the shape of the adhesion-slip curve. Notably, when the phase difference approaches 90°, the locomotive operates near the point of maximum adhesion, corresponding to the COF. Furthermore, the sensitivity of this approach to key system parameters, including normal load, wheel rolling radius, and modulation frequency, is examined. The findings provide valuable insights into the robustness and applicability of torque modulation-based COF estimation techniques in real-time traction control systems. The estimated COF can be further leveraged for adhesion management, driver advisory systems, and autonomous train operation.
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The coefficient of friction (COF), defined as the maximum of the adhesion coefficient for a given contact condition, fluctuates rapidly due to environmental and operational factors. This paper introduces a torque modulation-based method for COF estimation. A simplified analytical model of the Manchester benchmark bogie operating under dry adhesion conditions is used to evaluate this method. The study presents an analytical equation that confirms earlier simulation-based findings showing a phase difference between applied torque modulation and resulting motor angular velocity. This phase relationship is shown to reflect the shape of the adhesion-slip curve. Notably, when the phase difference approaches 90°, the locomotive operates near the point of maximum adhesion, corresponding to the COF. Furthermore, the sensitivity of this approach to key system parameters, including normal load, wheel rolling radius, and modulation frequency, is examined. The findings provide valuable insights into the robustness and applicability of torque modulation-based COF estimation techniques in real-time traction control systems. The estimated COF can be further leveraged for adhesion management, driver advisory systems, and autonomous train operation.
Detection of Rail Surface Defects based on Axle Box Acceleration Measurements
A Measurement Campaign in Sweden
Conference paper
(2024)
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Wassamon Phusakulkajorn, Jurjen Hendriks, Rolf Dollevoet, Zili Li, Jan Moraal, Chen Shen, Yuanchen Zeng, Siwarak Unsiwilai, Bojan Bogojevic, Matthias Asplund, Arjen Zoeteman, Alfredo Nunez
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.
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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.
Condition monitoring of railway transition zones using acceleration measurements on multiple axle boxes
Case studies in the Netherlands, Sweden, and Norway
Conference paper
(2024)
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Li Wang, Siwarak Unsiwilai, Yuanchen Zeng, Chen Shen, Jurjen Hendriks, Jan Moraal, Arjen Zoeteman, Alfredo Nunez, Rolf Dollevoet, Zili Li
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.
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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.
Artificial Intelligence in Railway Infrastructure
Current research, challenges, and future opportunities
Journal article
(2023)
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Wassamon Phusakulkajorn, Alfredo Nunez, Hongrui Wang, Ali Jamshidi, Arjen Zoeteman, Burchard Ripke, Rolf Dollevoet, Bart De Schutter, Zili Li
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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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.
Evaluating railway track stiffness using axle box accelerations
A digital twin approach
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.
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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.