R.P.B.J. Dollevoet
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13 records found
1
The Link Between the Rail Wear Rate and Rolling Contact Fatigue
From Bog Data Analysis to Lab Research
Defining and Evaluating the Comfort Braking Curve in European Train Control System for Operational Efficiency
Implementing the comfort braking curve without making concessions with respect to operational efficiency and safety
Despite its importance, academic literature on cross-border maintenance strategies is notably limited or non-existent. This thesis begins by describing the current situation of railway cross-border sections to identify the main challenges and existing coordination practices. It then proceeds to analyse these challenges through expert opinions gathered via a questionnaire. By highlighting the key issues and potential solutions, this research aims to fill the gap in the literature and provide a comprehensive framework for improving cross-border railway maintenance coordination.
This thesis also develops a digital and cooperative framework (DCF) to enhance cross-border maintenance decision support within the railway system of the European Union (EU), addressing the cross-border challenges holistically. It emphasizes the introduction of digital twin technology to meet the urgent need for infrastructure digitalization and coordination between the authorities and infrastructure managers in different countries, enabling the cooperative optimisation of the capacity of railway networks. The DCF consists of three components: the establishment of a European Railway Entity, the creation of a European Railway Forum, and the implementation of digital twin (DT) technology. The European Railway Entity aims to centralize coordination, streamline decision-making, and enforce standardized regulations across member states. The European Railway Forum focuses on fostering collaboration and knowledge sharing among stakeholders, facilitating continuous improvement in maintenance practices. The DT technology offers real-time data visualization, predictive maintenance, and advanced analytics to optimize maintenance operations and enhance infrastructure reliability, enabling an evidence-based decision-making process.
Each of the three components of the DCF is evaluated for its potential benefits and implementation challenges. The European Railway Entity can enhance the coordination and standardization of maintenance operations but may face resistance due to its hierarchical structure and potential conflicts with infrastructure managers whose national interests might be compromised for the sake of overall system performance. The European Railway Forum is cost-effective and practical, promoting voluntary data sharing and continuous improvement; however, suggestions and implementation involve complex procedures that require trust and the management of key confidential information. DT offers the greatest potential for innovation and future-readiness, but it demands a significant initial investment, robust data security measures, and the design of complex mechanisms to enable coordination between different DT platforms and protocols across countries.
The proposed DCF is discussed in two case studies. The first case study consists of a generic case of a bridge for railways between two countries focusing on the implementation and assessing potential benefits and limitations of DT as a tool of the DCF. The second case study addresses the cross-border situation in the Netherlands, proposing an implementation plan and evaluating potential advantages and disadvantages. Additionally, the Emmerich–Oberhausen maintenance project on the cross-border section between the Netherlands and Germany is analysed. This case study highlights the benefits, and challenges of implementing the proposed DCF.
In conclusion, the proposed digital and cooperative framework, integrating the European Railway Entity, the European Railway Forum, and DT technology, aims to support maintenance decisions and address the current challenges in cross-border railway maintenance. This integrated framework seeks to improve operational efficiency, increase network reliability, and support the sustainability goals of the EU, ultimately making the railway system more attractive to users. ...
Despite its importance, academic literature on cross-border maintenance strategies is notably limited or non-existent. This thesis begins by describing the current situation of railway cross-border sections to identify the main challenges and existing coordination practices. It then proceeds to analyse these challenges through expert opinions gathered via a questionnaire. By highlighting the key issues and potential solutions, this research aims to fill the gap in the literature and provide a comprehensive framework for improving cross-border railway maintenance coordination.
This thesis also develops a digital and cooperative framework (DCF) to enhance cross-border maintenance decision support within the railway system of the European Union (EU), addressing the cross-border challenges holistically. It emphasizes the introduction of digital twin technology to meet the urgent need for infrastructure digitalization and coordination between the authorities and infrastructure managers in different countries, enabling the cooperative optimisation of the capacity of railway networks. The DCF consists of three components: the establishment of a European Railway Entity, the creation of a European Railway Forum, and the implementation of digital twin (DT) technology. The European Railway Entity aims to centralize coordination, streamline decision-making, and enforce standardized regulations across member states. The European Railway Forum focuses on fostering collaboration and knowledge sharing among stakeholders, facilitating continuous improvement in maintenance practices. The DT technology offers real-time data visualization, predictive maintenance, and advanced analytics to optimize maintenance operations and enhance infrastructure reliability, enabling an evidence-based decision-making process.
Each of the three components of the DCF is evaluated for its potential benefits and implementation challenges. The European Railway Entity can enhance the coordination and standardization of maintenance operations but may face resistance due to its hierarchical structure and potential conflicts with infrastructure managers whose national interests might be compromised for the sake of overall system performance. The European Railway Forum is cost-effective and practical, promoting voluntary data sharing and continuous improvement; however, suggestions and implementation involve complex procedures that require trust and the management of key confidential information. DT offers the greatest potential for innovation and future-readiness, but it demands a significant initial investment, robust data security measures, and the design of complex mechanisms to enable coordination between different DT platforms and protocols across countries.
The proposed DCF is discussed in two case studies. The first case study consists of a generic case of a bridge for railways between two countries focusing on the implementation and assessing potential benefits and limitations of DT as a tool of the DCF. The second case study addresses the cross-border situation in the Netherlands, proposing an implementation plan and evaluating potential advantages and disadvantages. Additionally, the Emmerich–Oberhausen maintenance project on the cross-border section between the Netherlands and Germany is analysed. This case study highlights the benefits, and challenges of implementing the proposed DCF.
In conclusion, the proposed digital and cooperative framework, integrating the European Railway Entity, the European Railway Forum, and DT technology, aims to support maintenance decisions and address the current challenges in cross-border railway maintenance. This integrated framework seeks to improve operational efficiency, increase network reliability, and support the sustainability goals of the EU, ultimately making the railway system more attractive to users.
These PDEs could be leveraged to simulate the underlying scenarios. The dissertation introduces physics-informed machine learning (PIML) based approaches tailored to simulate the dynamics of beam structures. The aim is to incorporate the physical laws in the neural networks training for more accurate and realistic simulations, handle noisy data effectively, and improve prediction accuracy while mitigating challenges such as multiscale problems and generalization. Chapter 1 outlines the primary challenges tackled in the dissertation. Chapters 2 through 5 detail the methodologies developed to address each challenge.
Chapter 2 presents a physics-informed neural network (PINN) based methodology to simulate complex beam systems with real-world mate- rial properties. In addition, inverse problems are solved in the presence of noisy data to predict unknown parameters, including force acting on the beam systems. It is essential to consider the real-world material parameters to simulate the dynamics of the modeled system and ensure the digital model represents the ground truth. However, incorporating material characteristics leads to multiscale PDE coefficients in the physical model, posing difficulty in training for PINNs. Subsequently, a frame- work is proposed to incorporate nondimensional PDEs into the PINN loss function. This approach facilitates efficient forward and inverse simulations while robust to noise and uncertainty in measurement data. The efficacy of this approach is demonstrated through simulations of Euler- Bernoulli and Timoshenko beam systems, contributing to the challenge of simulating large-scale systems with multiple interconnected components.
Chapter 3 investigates beam dynamic simulations on Winkler foundations for large spatiotemporal domains using PIML. Predictions on expansive spatiotemporal domains are vital for structural integrity, design optimization, and control mechanisms. A causality-respecting PINN frame- work is introduced, enhancing prediction accuracy. Furthermore, integrating transfer learning addresses the need to re-train the network for different initial conditions and computational domains. Numerical experiments based on Euler-Bernoulli and Timoshenko theories validate the methodology for respecting the causality and generalizing the beam dynamics across similar problems. The approach efficiently predicts beam dynamics under diverse engineering scenarios, reducing computational costs and improving convergence.
Chapter 4 explores the generalization abilities of PIML, essential for practical applications requiring accurate predictions in unexplored regions. The proposed framework exploits the inherent causality in the PDE solutions by merging PIML models with recurrent neural architectures, namely neural oscillators. The neural ordinary differential equations in the form of neural oscillators effectively handle long-time dependencies and address gradient-related issues, fostering improved generalization in PIML tasks. Benchmark equations like viscous Burgers, Allen-Cahn, Schrödinger, and biharmonic Euler-Bernoulli beam equations are used to demonstrate the effectiveness of the proposed approach. Through ex- tensive experimentation with time-dependent nonlinear PDEs, the study showcases superior performance compared to existing state-of-the-art methods. The proposed method provides accurate solutions for extrapolation and prediction beyond the training data by enhancing the generalization capabilities of PIML, promising advancements in complex system simulations.
Chapter 5 follows up on generalization of beam dynamics beyond PIML- based approaches. Computer-aided simulations are crucial for advancing engineering industries, but existing simulators often struggle to generalize beyond their training domain. The chapter proposes a two-stage methodology to tackle this challenge. Firstly, it utilizes specialized simulators tailored to the application, such as causal PINNs and black-box finite element simulations. Secondly, it integrates predictions from the first stage into a recurrent neural architecture, incorporating ordinary differential equations to capture intrinsic dynamics and enhance generalization. The approach efficiently captures causality and generalizes dynamics across various data sources. Numerical experiments cover fundamental structural engineering scenarios, including real-world catenary contact wire uplift predictions, and demonstrate superior performance compared to conventional methods, and promise for diverse industrial applications. This dissertation concludes with Chapter 6.
In particular, this dissertation introduces PIML methodologies for simulating complex beam structures, addressing key challenges such as incorporating real material properties, handling noisy data, and improving prediction accuracy. Chapter 2 introduces a PINN-based methodology that efficiently simulates beam systems and predicts unknown parameters, mitigating the difficulties posed by multiscale PDE coefficients. Chapter 3 tackles the challenge of large-domain beam dynamics predictions on the Winkler foundations by using causality-respecting PINNs and integrating transfer learning to reduce computational costs. Chapter 4 addresses the challenge of out-of-domain predictions in PIML by introducing neural oscillators. Chapter 5 proposes a two-stage methodology to generalize beam dynamics simulations, integrating beam dynamics solvers and recurrent neural-based architectures, showcasing its efficacy in real-world applications such as catenary contact wire uplift predictions.
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These PDEs could be leveraged to simulate the underlying scenarios. The dissertation introduces physics-informed machine learning (PIML) based approaches tailored to simulate the dynamics of beam structures. The aim is to incorporate the physical laws in the neural networks training for more accurate and realistic simulations, handle noisy data effectively, and improve prediction accuracy while mitigating challenges such as multiscale problems and generalization. Chapter 1 outlines the primary challenges tackled in the dissertation. Chapters 2 through 5 detail the methodologies developed to address each challenge.
Chapter 2 presents a physics-informed neural network (PINN) based methodology to simulate complex beam systems with real-world mate- rial properties. In addition, inverse problems are solved in the presence of noisy data to predict unknown parameters, including force acting on the beam systems. It is essential to consider the real-world material parameters to simulate the dynamics of the modeled system and ensure the digital model represents the ground truth. However, incorporating material characteristics leads to multiscale PDE coefficients in the physical model, posing difficulty in training for PINNs. Subsequently, a frame- work is proposed to incorporate nondimensional PDEs into the PINN loss function. This approach facilitates efficient forward and inverse simulations while robust to noise and uncertainty in measurement data. The efficacy of this approach is demonstrated through simulations of Euler- Bernoulli and Timoshenko beam systems, contributing to the challenge of simulating large-scale systems with multiple interconnected components.
Chapter 3 investigates beam dynamic simulations on Winkler foundations for large spatiotemporal domains using PIML. Predictions on expansive spatiotemporal domains are vital for structural integrity, design optimization, and control mechanisms. A causality-respecting PINN frame- work is introduced, enhancing prediction accuracy. Furthermore, integrating transfer learning addresses the need to re-train the network for different initial conditions and computational domains. Numerical experiments based on Euler-Bernoulli and Timoshenko theories validate the methodology for respecting the causality and generalizing the beam dynamics across similar problems. The approach efficiently predicts beam dynamics under diverse engineering scenarios, reducing computational costs and improving convergence.
Chapter 4 explores the generalization abilities of PIML, essential for practical applications requiring accurate predictions in unexplored regions. The proposed framework exploits the inherent causality in the PDE solutions by merging PIML models with recurrent neural architectures, namely neural oscillators. The neural ordinary differential equations in the form of neural oscillators effectively handle long-time dependencies and address gradient-related issues, fostering improved generalization in PIML tasks. Benchmark equations like viscous Burgers, Allen-Cahn, Schrödinger, and biharmonic Euler-Bernoulli beam equations are used to demonstrate the effectiveness of the proposed approach. Through ex- tensive experimentation with time-dependent nonlinear PDEs, the study showcases superior performance compared to existing state-of-the-art methods. The proposed method provides accurate solutions for extrapolation and prediction beyond the training data by enhancing the generalization capabilities of PIML, promising advancements in complex system simulations.
Chapter 5 follows up on generalization of beam dynamics beyond PIML- based approaches. Computer-aided simulations are crucial for advancing engineering industries, but existing simulators often struggle to generalize beyond their training domain. The chapter proposes a two-stage methodology to tackle this challenge. Firstly, it utilizes specialized simulators tailored to the application, such as causal PINNs and black-box finite element simulations. Secondly, it integrates predictions from the first stage into a recurrent neural architecture, incorporating ordinary differential equations to capture intrinsic dynamics and enhance generalization. The approach efficiently captures causality and generalizes dynamics across various data sources. Numerical experiments cover fundamental structural engineering scenarios, including real-world catenary contact wire uplift predictions, and demonstrate superior performance compared to conventional methods, and promise for diverse industrial applications. This dissertation concludes with Chapter 6.
In particular, this dissertation introduces PIML methodologies for simulating complex beam structures, addressing key challenges such as incorporating real material properties, handling noisy data, and improving prediction accuracy. Chapter 2 introduces a PINN-based methodology that efficiently simulates beam systems and predicts unknown parameters, mitigating the difficulties posed by multiscale PDE coefficients. Chapter 3 tackles the challenge of large-domain beam dynamics predictions on the Winkler foundations by using causality-respecting PINNs and integrating transfer learning to reduce computational costs. Chapter 4 addresses the challenge of out-of-domain predictions in PIML by introducing neural oscillators. Chapter 5 proposes a two-stage methodology to generalize beam dynamics simulations, integrating beam dynamics solvers and recurrent neural-based architectures, showcasing its efficacy in real-world applications such as catenary contact wire uplift predictions.
Vibration-based railway track condition monitoring
A physics-based digital twin approach
Het doel van dit rapport is om antwoord te geven op de hoofdvraag: ’Hoe kan ballast een circulair materiaal worden?’
Ballast is wordt vaak gebruikt voor spoorwegen omdat het relatief goedkoop is om aan te leggen en omdat het makkelijk te onderhouden is. Ballast wordt altijd uit het buitenland geïmporteerd omdat het in Nederland niet voorkomt. Vaak uit Duitsland, Noorwegen of België.
Circulariteit staat voor het zo weinig mogelijk gebruiken van materiaal, het hoogwaardig hergebruiken van materiaal en het recyclen van materiaal. Om ballast circulair te laten worden moeten er dus ten eerste zo weinig mogelijk gebruikt worden, moet het zo vaak mogelijk voor de spoorwegen hergebruikt worden en moet het daarna op een andere manier hoogwaardig hergebruikt worden.
Dit wordt al voor een groot deel gedaan maar kan nog beter. Door middel van horren en zeven op locatie wordt al veel ballast hergebruikt voor de spoorwegen. Maar daarna wordt het vaak afgevoerd en niet op een hoogwaardige manier hergebruikt. Het gebeurt wel dat ballast wordt hergebruikt in beton maar het wordt ook vaak vermengd met puinkorrel.
Voor de circulariteit is het belangrijk dat er alles wordt hergebruikt, dus ook het kleinere grind en zand. Dit kan ook op veel manieren worden hergebruikt in bijvoorbeeld beton, asfalt of voor decoratief gebruik.
Tenslotte zijn er ook alternatieven voor ballast onderzocht, want ballast zou pas echt circulair zijn als het er helemaal niet meer is. Er kan bijvoorbeeld beton gebruikt worden, echter is dit veel duurder. Ook kunnen er metaalslakken gebruikt worden, alleen moet er nog meer onderzoek gedaan worden naar de vervuiling die het kan veroorzaken.
Kortom, ballast kan een circulair materiaal worden door alles op een hoogwaardige manier her te ge- bruiken. Waarbij er dus geen restproducten zijn, ook niet bij de tweede levenscyclus. Hierbij is het belangrijk dat het ballast niet zijn waarde verliest en dat het wordt hergebruikt voor iets wat aan gelijkwaardige waarde is of zelfs nog meer waard is, zoals beton of asfalt. ...
Het doel van dit rapport is om antwoord te geven op de hoofdvraag: ’Hoe kan ballast een circulair materiaal worden?’
Ballast is wordt vaak gebruikt voor spoorwegen omdat het relatief goedkoop is om aan te leggen en omdat het makkelijk te onderhouden is. Ballast wordt altijd uit het buitenland geïmporteerd omdat het in Nederland niet voorkomt. Vaak uit Duitsland, Noorwegen of België.
Circulariteit staat voor het zo weinig mogelijk gebruiken van materiaal, het hoogwaardig hergebruiken van materiaal en het recyclen van materiaal. Om ballast circulair te laten worden moeten er dus ten eerste zo weinig mogelijk gebruikt worden, moet het zo vaak mogelijk voor de spoorwegen hergebruikt worden en moet het daarna op een andere manier hoogwaardig hergebruikt worden.
Dit wordt al voor een groot deel gedaan maar kan nog beter. Door middel van horren en zeven op locatie wordt al veel ballast hergebruikt voor de spoorwegen. Maar daarna wordt het vaak afgevoerd en niet op een hoogwaardige manier hergebruikt. Het gebeurt wel dat ballast wordt hergebruikt in beton maar het wordt ook vaak vermengd met puinkorrel.
Voor de circulariteit is het belangrijk dat er alles wordt hergebruikt, dus ook het kleinere grind en zand. Dit kan ook op veel manieren worden hergebruikt in bijvoorbeeld beton, asfalt of voor decoratief gebruik.
Tenslotte zijn er ook alternatieven voor ballast onderzocht, want ballast zou pas echt circulair zijn als het er helemaal niet meer is. Er kan bijvoorbeeld beton gebruikt worden, echter is dit veel duurder. Ook kunnen er metaalslakken gebruikt worden, alleen moet er nog meer onderzoek gedaan worden naar de vervuiling die het kan veroorzaken.
Kortom, ballast kan een circulair materiaal worden door alles op een hoogwaardige manier her te ge- bruiken. Waarbij er dus geen restproducten zijn, ook niet bij de tweede levenscyclus. Hierbij is het belangrijk dat het ballast niet zijn waarde verliest en dat het wordt hergebruikt voor iets wat aan gelijkwaardige waarde is of zelfs nog meer waard is, zoals beton of asfalt.
Plastic railway sleepers
Creating a finite element model for hybrid plastic railway sleepers
Horizontal deformation of the High Speed Rail track
A study into the horizontal deformation of a sand embankment on asymmetrical soft soil for a hight speed rail track - case study HSL Rijpwetering
To reduce the life cycle cost and failure rate of catenary in practice, planned and predictive maintenance is desired based on the condition monitoring of catenary. However, the monitoring data are underutilized to effectively assess the catenary condition and facilitate maintenance decision-making. This dissertation contributes in improving the dynamic condition assessment of catenary using the data from condition monitoring. New performance indicators (PIs) of catenary are defined in a way that is adaptive to the variations of monitoring data measured under different circumstances, such as the changes of catenary structure, pantograph type and train speed. The relationship between the monitoring data and the contact wire irregularities is studied using historical data and simulations. Data-based approaches are developed for the quantitative assessment of dynamic catenary condition.
First, an intrinsic wavelength contained in the pantograph-catenary contact force is identified and defined as the catenary structure wavelength (CSW). It is caused by the periodic variations of contact wire stiffness attributed to the cyclic structure of catenary that must regulate the height of contact wire in every spans and interdropper distances. An approach that adaptively extracts the CSWs of pantograph-catenary contact force is proposed based on the empirical mode decomposition algorithm. It extracts the CSW signals corresponding to the span lengths and interdropper distances, respectively, summing to form a characteristic signal of CSWs. The residual signal of the contact force excluding the CSWs is regarded as the non-CSW signal. The mean and standard deviation of the CSWs signal are used as PIs to indicate the condition of the main catenary geometric parameters. A PI based on the quadratic time-frequency representation of the non-CSW signal is proposed for detecting and localizing the local irregularities of contact wire. The proposed PIs are tested by simulation and measurement data and proven effective and adaptive owning to the use of CSWs and non-CSW signal.
Second, the concept of CSW is expanded to the pantograph head acceleration from which the CSWs and non-CSW signal can also be extracted using the same approach developed for the contact force. Considering the characteristics of pantograph head acceleration, the wavelet packet entropy of the CSWs and non-CSW signal is proposed as PIs for detecting contact wire irregularities with different lengths. The entropy of CSWs is used for detecting irregularities with a length longer than 5 m, while the entropy of non-CSW signal is for the short-length local irregularities. An approach to detect and verify contact wire irregularities using the measurement data of pantograph head vertical acceleration from frequent inspections is proposed. The approach is tested using historical inspection data from which irregularities at all lengths are detected and verified. Maintenance resources can thus be specifically allocated to verified detection results to save cost and time.
Third, through analyzing historical inspection data and data-based simulation results, it is found that while the contact wire irregularity deteriorates the pantograph-catenary interaction, the formation of irregularity is also associated with the effects of the interaction like variations of contact and friction forces. Concretely, the contact wire height irregularity with an amplitude of 8 mm can cause considerable increase in the standard deviation of pantograph-catenary contact force. In addition, the irregularity with a certain wavelength can induce the dynamic response with the same wavelength in the contact force. This in turn makes the irregularity part deteriorating faster than the other parts of catenary. At a smaller scale, when the wear irregularity of contact wire has an average wire thickness loss of about 1.5 mm, it can also increase the standard deviation of contact force by more than 5%. Due to the fixing effect at the registration arms and droppers, the wear irregularity commonly contains structural wavelengths of catenary including span lengths and interdropper distances. It is also found that the wear irregularity tends to grow and spread toward in the common or dominant running direction of trains in the specific line. Nevertheless, an existing defect may not affect every pantograph passage and every type of data measured. It is thus advised to measure multiple types of data and perform more frequent inspections to avoid undetected defects.
Last, a data-driven approach using the Bayesian network (BN) to fuse the available inspection data of catenary into an integrated PI is proposed. The BN topology is first structured based on the physical relations between five data types including the train speed, dynamic stagger and height of contact wire, pantograph head acceleration, and pantograph-catenary contact force. Then, tailored PIs are individually defined and extracted from the five types of data as the BN input. As the output of BN, an integrated PI is defined as the overall condition level of catenary considering all defects that can be reflected by the five types of data. Finally, using historical inspections data and maintenance records from a section of high-speed line, the BN parameters are estimated to establish a probabilistic relationship between the input and the output PI. By testing the BN-based approach using new inspection data from the same railway line, it is shown that the integrated PI can adequately represent the catenary condition, leading to considerable reduction in the false alarm rate of catenary defect detection compared with the current practice. The approach can also work acceptably with noisy or partly missing data.
In summary, this dissertation answers how to adequately transform the condition monitoring data of catenary into quantitative assessments of the dynamic catenary condition. The proposed approaches are intended for generic implementations in railway catenaries worldwide. ...
To reduce the life cycle cost and failure rate of catenary in practice, planned and predictive maintenance is desired based on the condition monitoring of catenary. However, the monitoring data are underutilized to effectively assess the catenary condition and facilitate maintenance decision-making. This dissertation contributes in improving the dynamic condition assessment of catenary using the data from condition monitoring. New performance indicators (PIs) of catenary are defined in a way that is adaptive to the variations of monitoring data measured under different circumstances, such as the changes of catenary structure, pantograph type and train speed. The relationship between the monitoring data and the contact wire irregularities is studied using historical data and simulations. Data-based approaches are developed for the quantitative assessment of dynamic catenary condition.
First, an intrinsic wavelength contained in the pantograph-catenary contact force is identified and defined as the catenary structure wavelength (CSW). It is caused by the periodic variations of contact wire stiffness attributed to the cyclic structure of catenary that must regulate the height of contact wire in every spans and interdropper distances. An approach that adaptively extracts the CSWs of pantograph-catenary contact force is proposed based on the empirical mode decomposition algorithm. It extracts the CSW signals corresponding to the span lengths and interdropper distances, respectively, summing to form a characteristic signal of CSWs. The residual signal of the contact force excluding the CSWs is regarded as the non-CSW signal. The mean and standard deviation of the CSWs signal are used as PIs to indicate the condition of the main catenary geometric parameters. A PI based on the quadratic time-frequency representation of the non-CSW signal is proposed for detecting and localizing the local irregularities of contact wire. The proposed PIs are tested by simulation and measurement data and proven effective and adaptive owning to the use of CSWs and non-CSW signal.
Second, the concept of CSW is expanded to the pantograph head acceleration from which the CSWs and non-CSW signal can also be extracted using the same approach developed for the contact force. Considering the characteristics of pantograph head acceleration, the wavelet packet entropy of the CSWs and non-CSW signal is proposed as PIs for detecting contact wire irregularities with different lengths. The entropy of CSWs is used for detecting irregularities with a length longer than 5 m, while the entropy of non-CSW signal is for the short-length local irregularities. An approach to detect and verify contact wire irregularities using the measurement data of pantograph head vertical acceleration from frequent inspections is proposed. The approach is tested using historical inspection data from which irregularities at all lengths are detected and verified. Maintenance resources can thus be specifically allocated to verified detection results to save cost and time.
Third, through analyzing historical inspection data and data-based simulation results, it is found that while the contact wire irregularity deteriorates the pantograph-catenary interaction, the formation of irregularity is also associated with the effects of the interaction like variations of contact and friction forces. Concretely, the contact wire height irregularity with an amplitude of 8 mm can cause considerable increase in the standard deviation of pantograph-catenary contact force. In addition, the irregularity with a certain wavelength can induce the dynamic response with the same wavelength in the contact force. This in turn makes the irregularity part deteriorating faster than the other parts of catenary. At a smaller scale, when the wear irregularity of contact wire has an average wire thickness loss of about 1.5 mm, it can also increase the standard deviation of contact force by more than 5%. Due to the fixing effect at the registration arms and droppers, the wear irregularity commonly contains structural wavelengths of catenary including span lengths and interdropper distances. It is also found that the wear irregularity tends to grow and spread toward in the common or dominant running direction of trains in the specific line. Nevertheless, an existing defect may not affect every pantograph passage and every type of data measured. It is thus advised to measure multiple types of data and perform more frequent inspections to avoid undetected defects.
Last, a data-driven approach using the Bayesian network (BN) to fuse the available inspection data of catenary into an integrated PI is proposed. The BN topology is first structured based on the physical relations between five data types including the train speed, dynamic stagger and height of contact wire, pantograph head acceleration, and pantograph-catenary contact force. Then, tailored PIs are individually defined and extracted from the five types of data as the BN input. As the output of BN, an integrated PI is defined as the overall condition level of catenary considering all defects that can be reflected by the five types of data. Finally, using historical inspections data and maintenance records from a section of high-speed line, the BN parameters are estimated to establish a probabilistic relationship between the input and the output PI. By testing the BN-based approach using new inspection data from the same railway line, it is shown that the integrated PI can adequately represent the catenary condition, leading to considerable reduction in the false alarm rate of catenary defect detection compared with the current practice. The approach can also work acceptably with noisy or partly missing data.
In summary, this dissertation answers how to adequately transform the condition monitoring data of catenary into quantitative assessments of the dynamic catenary condition. The proposed approaches are intended for generic implementations in railway catenaries worldwide.