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Urban mobility is increasingly becoming accepted as a basic human need, as socio-economic opportunities depend on the ability to reach places within an acceptable time. Conversely, the emergence of megalopoleis as dominant features of the global landscape has increased commuting effort to unprecedented levels, due to the ever expanding urban areas and the associated travel distances. This now poses a risk to the efficient accessibility of cities, but there is an assumption that the problem can be overcome by increasing the speed of transport systems. However, advocates of this approach overlook important utility trade-offs that arise from the conflict between greater vehicle speeds and the additional time required to access the services. In this paper, we investigate this approach and show that higher speeds in metro systems do not always result in faster travel in cities. We then propose a new approach to addressing the problem, which culminates in a solution that can overcome the current paradoxes and increase door-to-door speeds more effectively. The resulting operational concept optimizes speed and coverage in urban rail systems in megalopoleis, accommodating the longer trips within time budgets. We position this research as a starting point to a new perspective on developing complex urban systems in the future.
Marcelo Blumenfeld; Clive Roberts; Felix Schmid. Using Radical Innovation to Overcome Utility Trade-Offs in Urban Rail Systems in Megalopoleis. Future Transportation 2021, 1, 154 -168.
AMA StyleMarcelo Blumenfeld, Clive Roberts, Felix Schmid. Using Radical Innovation to Overcome Utility Trade-Offs in Urban Rail Systems in Megalopoleis. Future Transportation. 2021; 1 (2):154-168.
Chicago/Turabian StyleMarcelo Blumenfeld; Clive Roberts; Felix Schmid. 2021. "Using Radical Innovation to Overcome Utility Trade-Offs in Urban Rail Systems in Megalopoleis." Future Transportation 1, no. 2: 154-168.
Switch and crossing (S&C) faults are a major cause of track-related delays and account for a significant proportion of maintenance and renewal budgets for railway infrastructure managers around the world. Although various modelling approaches have been proposed in the literature for the simulation of vehicle–track dynamic interaction, wheel–rail contact and damage prediction, there is a lack of evaluation for combining these approaches to effectively predict the failure mechanism. An evaluation of S&C modelling approaches has therefore been performed in this article to justify their selection for the research interests of predicting the most dominant failure mechanisms of wear, rolling contact fatigue (RCF) and plastic deformation in S&C rails by recognising the factors that influence the accuracy and efficiency of the proposed modelling approaches. A detailed discussion of the important modelling aspects has been carried out by considering the effectiveness of each individual approach and the combination of different approaches, along with a suggestion of appropriate modelling approaches for predicting the dominant failure mechanisms.
Nikhil Pillai; Jou-Yi Shih; Clive Roberts. Evaluation of Numerical Simulation Approaches for Simulating Train–Track Interactions and Predicting Rail Damage in Railway Switches and Crossings (S&Cs). Infrastructures 2021, 6, 63 .
AMA StyleNikhil Pillai, Jou-Yi Shih, Clive Roberts. Evaluation of Numerical Simulation Approaches for Simulating Train–Track Interactions and Predicting Rail Damage in Railway Switches and Crossings (S&Cs). Infrastructures. 2021; 6 (5):63.
Chicago/Turabian StyleNikhil Pillai; Jou-Yi Shih; Clive Roberts. 2021. "Evaluation of Numerical Simulation Approaches for Simulating Train–Track Interactions and Predicting Rail Damage in Railway Switches and Crossings (S&Cs)." Infrastructures 6, no. 5: 63.
Rail profile measurement is one of the most critical tasks for track quality inspection to ensure the safe operation of track systems. In modern railway systems, laser triangulation sensors have been widely adopted in onboard measuring units to carry out 2-D rail profile measurement due to their robustness and truly noncontact properties. However, existing solutions limit the degrees of freedom of the laser sensor and thus cannot provide full coverage of the rail profile due to the “shadowing effect” of triangulation sensors. Incomplete profiles limit the performance of wear assessment hence more detailed inspections still rely on contact-based tools operated by humans. These processes are time-consuming and incompatible with the ever-shortening maintenance windows available in modern railway systems. Benefiting from the miniaturization of sensing technology and improving processors, multisensing systems combine the strengths of different sensors. This article presents a new solution for a laser-based multisensing system for noncontact rail profile measurement and wear inspection. The addition of an inertial measurement unit (IMU) and a camera module allows portable rail profile measurement without “blind spots.” Optimized iterative closest point (ICP) registration is then applied to generate a complete representation of the rail profile. Experimental results demonstrate that the proposed system can provide accurate and efficient rail profile measurement, and could potentially replace conventional contact-based inspection tools.
Jiaqi Ye; Edward Stewart; Dingcheng Zhang; Qianyu Chen; Karthik Thangaraj; Clive Roberts. Integration of Multiple Sensors for Noncontact Rail Profile Measurement and Inspection. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -12.
AMA StyleJiaqi Ye, Edward Stewart, Dingcheng Zhang, Qianyu Chen, Karthik Thangaraj, Clive Roberts. Integration of Multiple Sensors for Noncontact Rail Profile Measurement and Inspection. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-12.
Chicago/Turabian StyleJiaqi Ye; Edward Stewart; Dingcheng Zhang; Qianyu Chen; Karthik Thangaraj; Clive Roberts. 2021. "Integration of Multiple Sensors for Noncontact Rail Profile Measurement and Inspection." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-12.
An improved method with feature extraction and neural network classification is proposed and applied to address the fault diagnosis problem in railway switch systems. Features are extracted by analysing the non-stationary signal time-frequency characteristics of the electro-mechanical machine under different fault severities. To enhance the fault diagnosis capability, an Energy-based Thresholding Wavelets (EBTW) approach is proposed by reconstructing a low-dimensional feature vector with selected parameters. Different from the conventional discrete wavelet transform and soft-thresholding wavelet transform, the proposed method localizes and redistributes the signal energy to achieve an efficient dimension reduction. The first step of the proposed method is to perform a wavelet transform upon the original signals, whose wavelet coefficients are then thresholded and an extraction of features is made, on the basis of the energy conservation property of wavelet transforms. Once the features are extracted and selected, several state-of-the-art neural-network-based classifiers are applied for fault diagnosis by identifying the signal features from a machine normal operation and faulty operations with multiple severities. The effectiveness of the proposed method is verified using real-world operational railway switch data and comparing with conventional feature extraction methods under different classifiers.
Qianyu Chen; Gemma Nicholson; Clive Roberts; Jiaqi Ye; Yihong Zhao. Improved Fault Diagnosis of Railway Switch System Using Energy-Based Thresholding Wavelets (EBTW) and Neural Networks. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -12.
AMA StyleQianyu Chen, Gemma Nicholson, Clive Roberts, Jiaqi Ye, Yihong Zhao. Improved Fault Diagnosis of Railway Switch System Using Energy-Based Thresholding Wavelets (EBTW) and Neural Networks. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-12.
Chicago/Turabian StyleQianyu Chen; Gemma Nicholson; Clive Roberts; Jiaqi Ye; Yihong Zhao. 2020. "Improved Fault Diagnosis of Railway Switch System Using Energy-Based Thresholding Wavelets (EBTW) and Neural Networks." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-12.
Recent developments in the area of condition monitoring research have been targeted towards predicting machinery health condition for the purpose of preventative maintenance. Typically, published research uses data collected from rotating components (bearings, cutting tools, etc.) working in an idealized lab environment as the case study for prognosis algorithm validations. However, the operational implementation in industry is still very sporadic, mainly owing to the lack of proper data allowing sufficiently mature development of comprehensive methodologies. The prognosis methodology presented herein bridges the gap between academic research and industrial implementations by employing a novel time period for prognosis and implementing random coefficients regression models. The definition of the remaining maintenance-free operating period (RMFOP) is proposed first, which helps to transform the usefulness of the degradation data that is readily available from data short of failure. Degradation patterns are subsequently extracted from the original degradation data, before fitting into either of two regression models (linear or exponential). The system residual life distributions are then computed and updated by estimating the parameter statistics within the model. This RMFOP-based methodology is validated using real-world degradation data collected from multiple operational railway switch systems across Great Britain. The results indicate that both the linear model and the exponential model can produce residual life distributions with a sufficient prediction accuracy for this specific application. The exponential model gives better predictions, the accuracy of which also improves as more of system life percentage has elapsed. By using the RMFOP methodology, switch system health condition affected by an incipient overdriving fault is recognized and predicted.
Qianyu Chen; Gemma Nicholson; Jiaqi Ye; Yihong Zhao; Clive Roberts. Estimating Residual Life Distributions of Complex Operational Systems Using a Remaining Maintenance Free Operating Period (RMFOP)-Based Methodology. Sensors 2020, 20, 5504 .
AMA StyleQianyu Chen, Gemma Nicholson, Jiaqi Ye, Yihong Zhao, Clive Roberts. Estimating Residual Life Distributions of Complex Operational Systems Using a Remaining Maintenance Free Operating Period (RMFOP)-Based Methodology. Sensors. 2020; 20 (19):5504.
Chicago/Turabian StyleQianyu Chen; Gemma Nicholson; Jiaqi Ye; Yihong Zhao; Clive Roberts. 2020. "Estimating Residual Life Distributions of Complex Operational Systems Using a Remaining Maintenance Free Operating Period (RMFOP)-Based Methodology." Sensors 20, no. 19: 5504.
Welcome to the WIT Press eLibrary - the home of the Transactions of the Wessex Institute collection, providing on-line access to papers presented at the Institute's prestigious international conferences and from its State-of-the-Art in Science & Engineering publications.
John Armstrong; Georgios Rempelos; Jingfu Wei; John Preston; Simon Blainey; John Easton; Clive Roberts. DEVELOPING A GENERALISED ASSESSMENT FRAMEWORK FOR RAILWAY INTERVENTIONS. Computers in Railways XVII 2020, 1 .
AMA StyleJohn Armstrong, Georgios Rempelos, Jingfu Wei, John Preston, Simon Blainey, John Easton, Clive Roberts. DEVELOPING A GENERALISED ASSESSMENT FRAMEWORK FOR RAILWAY INTERVENTIONS. Computers in Railways XVII. 2020; ():1.
Chicago/Turabian StyleJohn Armstrong; Georgios Rempelos; Jingfu Wei; John Preston; Simon Blainey; John Easton; Clive Roberts. 2020. "DEVELOPING A GENERALISED ASSESSMENT FRAMEWORK FOR RAILWAY INTERVENTIONS." Computers in Railways XVII , no. : 1.
Ning Zhao; Zhongbei Tian; Lei Chen; Clive Roberts; Stuart Hillmansen. Driving Strategy Optimization and Field Test on an Urban Rail Transit System. IEEE Intelligent Transportation Systems Magazine 2020, 13, 34 -44.
AMA StyleNing Zhao, Zhongbei Tian, Lei Chen, Clive Roberts, Stuart Hillmansen. Driving Strategy Optimization and Field Test on an Urban Rail Transit System. IEEE Intelligent Transportation Systems Magazine. 2020; 13 (3):34-44.
Chicago/Turabian StyleNing Zhao; Zhongbei Tian; Lei Chen; Clive Roberts; Stuart Hillmansen. 2020. "Driving Strategy Optimization and Field Test on an Urban Rail Transit System." IEEE Intelligent Transportation Systems Magazine 13, no. 3: 34-44.
This paper addresses the distributed filter design problem for a class of dynamic systems over wireless sensor networks. The missing measurements and the correlation among state noises and measurement noises are considered, where a set of mutually uncorrelated random variables is employed to describe the missing phenomena. Firstly, the construction of the designed filter is proposed and the prediction of the state at each node is given. Then, the filtering error covariance is presented and the filter parameters are determined to minimize the trace of such a covariance, where the network topology data are used to simplified the singular matrix. Subsequently, the relationship between the filter performance and missing probability of the measurement is discussed. Finally, a numerical simulation is presented to illustrate the effectiveness and capability of the proposed distributed filters.
Tao Wen; Chuanbo Wen; Clive Roberts; Baigen Cai. Distributed filtering for a class of discrete-time systems over wireless sensor networks. Journal of the Franklin Institute 2020, 357, 1 .
AMA StyleTao Wen, Chuanbo Wen, Clive Roberts, Baigen Cai. Distributed filtering for a class of discrete-time systems over wireless sensor networks. Journal of the Franklin Institute. 2020; 357 (5):1.
Chicago/Turabian StyleTao Wen; Chuanbo Wen; Clive Roberts; Baigen Cai. 2020. "Distributed filtering for a class of discrete-time systems over wireless sensor networks." Journal of the Franklin Institute 357, no. 5: 1.
Roller bearings form key components in many machines and, as such, their health status can directly influence the operation of the entire machine. Acoustic signals collected from roller bearings contain information on their health status. Hence, acoustic-based fault diagnosis techniques can provide novel solutions as condition monitoring tools for roller bearings. Traditionally, acoustic fault diagnosis methods have been based on conventional signal processing methods in which prior expert knowledge has been required in order to extract and interpret the health information contained within the collected acoustic signals. As an alternative, deep learning methods can be used to obtain heath information from the collected signals by constructing ‘end-to-end’ models that do not rely on prior knowledge. These approaches have been successfully applied in the condition monitoring of industrial machinery. However, conventional deep learning methods can only learn features from the vertices of input data and thereby ignore the information contained in the relationships (edges) between vertices. In this paper, which combines graph convolution operators, graph coarsening methods, and graph pooling operations; a deep graph convolutional network (DGCN) based on graph theory is applied to deliver acoustic-based fault diagnosis of roller bearings. In the proposed method, the collected acoustic signals are first transformed into graphs with geometric structures. The edge weights represent the similarity between connected vertices, which enriches the input information and hence improves the classification accuracy of the deep learning methods applied. To verify the effectiveness of the proposed system, experiments with roller bearings of varying condition were carried out in the laboratory. The experimental results demonstrate that the DGCN method can be used to detect different kinds and severities of faults in roller bearings by learning from the constructed graphs. The results have been compared to those obtained using other, conventional, deep learning methods applied to the same datasets. These comparative tests demonstrate improved classification accuracy when using the DGCN method.
Dingcheng Zhang; Edward Stewart; Mani Entezami; Clive Roberts; Dejie Yu. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement 2020, 156, 107585 .
AMA StyleDingcheng Zhang, Edward Stewart, Mani Entezami, Clive Roberts, Dejie Yu. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement. 2020; 156 ():107585.
Chicago/Turabian StyleDingcheng Zhang; Edward Stewart; Mani Entezami; Clive Roberts; Dejie Yu. 2020. "Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network." Measurement 156, no. : 107585.
Low-income countries (LICs) in Sub-Saharan Africa and South Asia are investing in new railway lines to replace deteriorated infrastructure from the 19th and 20th century. These actions, despite financial and economic constraints, have been justified in common visions of continent-wide efficient networks to cope with the demands of growing populations. However, most of the recent rail infrastructure projects are driven by international suppliers’ preferences and financing rather than creating railways that match the requirements of interoperable regional networks. This paper therefore explores the current status of rail infrastructure in these LICs and the operational performance achieved to understand specific capability gaps in each regional network. Drawing from the experience of European countries in transforming regional future visions into applied research, a technical strategy for rail infrastructure in LICs is proposed. The strategy captures the key capabilities to be addressed in order to achieve future performance goals, while emphasizing the need for emerging technologies to be used in fit-for-purpose solutions. It is envisioned that the strategy will provide the basis for the development of continental technical strategy programs with specific technology roadmaps towards a common goal.
Marcelo Blumenfeld; Wendy Wemakor; Labib Azzouz; Clive Roberts. Developing a New Technical Strategy for Rail Infrastructure in Low-Income Countries in Sub-Saharan Africa and South Asia. Sustainability 2019, 11, 4319 .
AMA StyleMarcelo Blumenfeld, Wendy Wemakor, Labib Azzouz, Clive Roberts. Developing a New Technical Strategy for Rail Infrastructure in Low-Income Countries in Sub-Saharan Africa and South Asia. Sustainability. 2019; 11 (16):4319.
Chicago/Turabian StyleMarcelo Blumenfeld; Wendy Wemakor; Labib Azzouz; Clive Roberts. 2019. "Developing a New Technical Strategy for Rail Infrastructure in Low-Income Countries in Sub-Saharan Africa and South Asia." Sustainability 11, no. 16: 4319.
Roller bearings are one of the most safety-critical components in many machines. Predicting the vibration-based remaining useful life (RUL) of roller bearings allows operators to make informed maintenance decisions and to guarantee reliability and safety. The health indices (HIs) for degradation assessment are constructed by extracting feature information from the collected data, which significantly influences the prognosis result. Conventional HI construction methods rely heavily on expert knowledge and also have limited capacity for learning health information from the raw data from roller bearings. Furthermore, outlier regions often occur in HIs developed by those methods, and these can easily result in false alarms. To address these problems, a novel HI construction method based on a deep multilayer perceptron (MLP) convolution neural network (DMLPCNN) model, which also considers outlier regions, is proposed in this paper. In the proposed model, a 1-D MLP convolution (Mlpconv) block, consisting of a convolution layer and a micro network, is applied to learn features directly from vibrational data. The learned features are then mapped into an HI using a global average pooling layer and a logistic regression layer. Finally, an outlier region correction method, based on sliding thresholds, is proposed to detect and remove outliers in the HI. The outlier region correction method is able to enhance the interpretability of the constructed HI. The effectiveness of the proposed method is verified using whole-life datasets of 17 bearings. The experimental results demonstrate that the proposed method outperforms conventional methods.
Dingcheng Zhang; Edward Stewart; Jiaqi Ye; Mani Entezami; Clive Roberts. Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions. IEEE Transactions on Instrumentation and Measurement 2019, 69, 2996 -3004.
AMA StyleDingcheng Zhang, Edward Stewart, Jiaqi Ye, Mani Entezami, Clive Roberts. Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions. IEEE Transactions on Instrumentation and Measurement. 2019; 69 (6):2996-3004.
Chicago/Turabian StyleDingcheng Zhang; Edward Stewart; Jiaqi Ye; Mani Entezami; Clive Roberts. 2019. "Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions." IEEE Transactions on Instrumentation and Measurement 69, no. 6: 2996-3004.
With the increasing concerns on energy consumption and operating cost in metro systems, energy saving on train operation attracts significant attentions. Previous studies have mainly focused on optimal control of a single train and energy-efficient train timetabling. The former does not consider the synchronization of motoring and braking trains, which cannot ensure the proper utilization of regenerative energy on the metro lines without energy storage systems. The latter includes scheduling train operations to synchronize motoring and braking trains for the better utilization of regenerative energy. However, the overlapping time of motoring and braking trains is usually as short as a few seconds and the energy reduction might be made impossible by train delays, which are common in practice. This paper presents a model framework, on the extents of motoring/braking of train acceleration and station stopping, as well as the locations of switching train operation modes, for real-time cooperative control of multiple metro trains. The objective is to minimize the net energy consumption with the consideration of utilizing regenerative energy. A cooperative co-evolutionary algorithm is developed to attain the solution of the proposed model. Case studies on a real-life metro line demonstrate the energy saving performance of the proposed approach compared with separate train control and timetable optimization, from no disturbance to a good range of delays. The results also indicate that the partial motoring in train acceleration and partial braking in station stopping achieve better net energy reduction, in comparison with full motoring/braking preferred in previous studies.
Yun Bai; Yunwen Cao; Zhao Yu; Tin Kin Ho; Clive Roberts; Baohua Mao. Cooperative Control of Metro Trains to Minimize Net Energy Consumption. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 2063 -2077.
AMA StyleYun Bai, Yunwen Cao, Zhao Yu, Tin Kin Ho, Clive Roberts, Baohua Mao. Cooperative Control of Metro Trains to Minimize Net Energy Consumption. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (5):2063-2077.
Chicago/Turabian StyleYun Bai; Yunwen Cao; Zhao Yu; Tin Kin Ho; Clive Roberts; Baohua Mao. 2019. "Cooperative Control of Metro Trains to Minimize Net Energy Consumption." IEEE Transactions on Intelligent Transportation Systems 21, no. 5: 2063-2077.
The railway relay plays an important role in railway systems. Its reliability has a significant effect on the safety of passengers and train operation, which can be reflected using degradation parameters. In this paper, a novel hybrid method based on the multi-layer decomposition and the radial basis function neural network (RBFNN) is proposed for life prediction of railway relays. As the degradation parameter series are usually nonlinear and non-stationary, it is vital to develop an essential method to preprocess the degradation series. In order to improve the prediction accuracy, a multi-layer decomposition method is developed first for data pre-processing, which blends complete ensemble empirical mode decomposition (CEEMD) and an improved variational mode decomposition (IVMD) with a stopping criterion for determining the decomposition modes number. It is noted that IVMD is then used to decompose the high-frequency intrinsic mode functions (IMFs) obtained using CEEMD to improve the prediction accuracy. Furthermore, RBFNN is applied to all the components for prediction. And the prediction results of all the components are reconstructed as the predicted degradation series. Finally, the effectiveness and robustness of the proposed novel hybrid prediction method are verified on one-step prediction and multi-step prediction by comparing other commonly used prediction methods. The experimental results indicate that the proposed hybrid prediction method performs best on the complex degradation parameters of safety relays.
Yongkui Sun; Yuan Cao; Mingjun Zhou; Tao Wen; Peng Li; Clive Roberts. A Hybrid Method for Life Prediction of Railway Relays Based on Multi-Layer Decomposition and RBFNN. IEEE Access 2019, 7, 44761 -44770.
AMA StyleYongkui Sun, Yuan Cao, Mingjun Zhou, Tao Wen, Peng Li, Clive Roberts. A Hybrid Method for Life Prediction of Railway Relays Based on Multi-Layer Decomposition and RBFNN. IEEE Access. 2019; 7 ():44761-44770.
Chicago/Turabian StyleYongkui Sun; Yuan Cao; Mingjun Zhou; Tao Wen; Peng Li; Clive Roberts. 2019. "A Hybrid Method for Life Prediction of Railway Relays Based on Multi-Layer Decomposition and RBFNN." IEEE Access 7, no. : 44761-44770.
For metro systems in over-crowded conditions, when an unexpected disturbance occurs, the operation of trains might be disturbed due to the high frequency and density of the metro traffic. A large number of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, by introducing binary variables as selection indicators for ATO profiles which were preset in on-board ATO systems by metro signal suppliers, we develop a mixed integer programming (MIP) model for a metro train timetable rescheduling problem in order to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. We formulate the total energy consumption as the difference between the tractive energy consumption and the regenerated energy by considering the mass of in-vehicle passengers. Then, we adopt commercial optimization software CPLEX to solve the proposed model, which can obtain tradeoff solutions in a short time. Finally, three numerical experiments based on real-world operational data are carried out to verify the effectiveness of the proposed method.
Zhuopu Hou; Hairong Dong; Shigen Gao; Gemma Nicholson; Lei Chen; Clive Roberts. Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow. IEEE Transactions on Intelligent Transportation Systems 2019, 20, 2774 -2785.
AMA StyleZhuopu Hou, Hairong Dong, Shigen Gao, Gemma Nicholson, Lei Chen, Clive Roberts. Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow. IEEE Transactions on Intelligent Transportation Systems. 2019; 20 (7):2774-2785.
Chicago/Turabian StyleZhuopu Hou; Hairong Dong; Shigen Gao; Gemma Nicholson; Lei Chen; Clive Roberts. 2019. "Energy-Saving Metro Train Timetable Rescheduling Model Considering ATO Profiles and Dynamic Passenger Flow." IEEE Transactions on Intelligent Transportation Systems 20, no. 7: 2774-2785.
Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems and optimised fleet availability. This paper is a review of the state of the art in condition monitoring systems for rolling element bearings, especially the axlebox bearings. This includes exploring the sensing technologies, summarising the main signal processing methods and condition monitoring techniques, i.e. wayside and on-board. Examples of commercially available systems and outputs of current research work are presented. The effectiveness of the current monitoring technologies is assessed and the p– f curve is presented. It is concluded that the research and practical tests on axlebox bearing monitoring are limited compared to the generic bearing applications.
Mani Entezami; Clive Roberts; Paul Weston; Edward Stewart; Arash Amini; Mayorkinos Papaelias. Perspectives on railway axle bearing condition monitoring. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2019, 234, 17 -31.
AMA StyleMani Entezami, Clive Roberts, Paul Weston, Edward Stewart, Arash Amini, Mayorkinos Papaelias. Perspectives on railway axle bearing condition monitoring. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. 2019; 234 (1):17-31.
Chicago/Turabian StyleMani Entezami; Clive Roberts; Paul Weston; Edward Stewart; Arash Amini; Mayorkinos Papaelias. 2019. "Perspectives on railway axle bearing condition monitoring." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 1: 17-31.
This paper presents the development of SmartDrive package to achieve the application of energy-efficient driving strategy. The results are from collaboration between Ricardo Rail and the Birmingham Centre for Railway Research and Education (BCRRE). Advanced tram and train trajectory optimization techniques developed by BCRRE as part of the UKTRAM More Energy Efficiency Tram project have been now incorporated in Ricardo's SmartDrive product offering. The train trajectory optimization method, associated driver training and awareness package (SmartDrive) has been developed for use on tram, metro, and some heavy rail systems. A simulator was designed that can simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on a typical AC or DC powered route. The energy evaluation results from the simulator will provide several potential energy-saving solutions for the existing route. An enhanced Brute Force algorithm was developed to achieve the optimization quickly and efficiently. Analysis of the results showed that by implementing an optimal speed trajectory, the energy usage in the network can be significantly reduced. A driver practical training system and the optimized lineside driving control signage, based on the optimized trajectory were developed for testing. This system instructed drivers to maximize coasting in segregated sections of the network and to match optimal speed limits in busier street sections. The field trials and real daily operations in the Edinburgh Tram Line, U.K., have shown that energy savings of 10%-20% are achievable.
Zhongbei Tian; Ning Zhao; Stuart Hillmansen; Clive Roberts; Trevor Dowens; Colin Kerr. SmartDrive: Traction Energy Optimization and Applications in Rail Systems. IEEE Transactions on Intelligent Transportation Systems 2019, 20, 2764 -2773.
AMA StyleZhongbei Tian, Ning Zhao, Stuart Hillmansen, Clive Roberts, Trevor Dowens, Colin Kerr. SmartDrive: Traction Energy Optimization and Applications in Rail Systems. IEEE Transactions on Intelligent Transportation Systems. 2019; 20 (7):2764-2773.
Chicago/Turabian StyleZhongbei Tian; Ning Zhao; Stuart Hillmansen; Clive Roberts; Trevor Dowens; Colin Kerr. 2019. "SmartDrive: Traction Energy Optimization and Applications in Rail Systems." IEEE Transactions on Intelligent Transportation Systems 20, no. 7: 2764-2773.
In railway condition monitoring, feature classification is a very critical step, and the extracted features are used to classify the types and levels of the faults. To achieve better accuracy and efficiency in the classification, the extracted features must be properly selected. In this paper, maximal information coefficient is employed in two different stages to establish a new feature selection method. By using this proposed two-stage feature selection method, strong features with low redundancy are reserved as the optimal feature subset, which results in the classification process having a more moderate computational cost and good overall performance. To evaluate this proposed two-stage selection method and prove its advantages over others, a case study focusing on the rolling bearing is carried out. The result shows that the proposed selection method can achieve a satisfactory overall classification performance with low-computational cost.
Tao Wen; Deyi Dong; Qianyu Chen; Lei Chen; Clive Roberts. Maximal Information Coefficient-Based Two-Stage Feature Selection Method for Railway Condition Monitoring. IEEE Transactions on Intelligent Transportation Systems 2019, 20, 2681 -2690.
AMA StyleTao Wen, Deyi Dong, Qianyu Chen, Lei Chen, Clive Roberts. Maximal Information Coefficient-Based Two-Stage Feature Selection Method for Railway Condition Monitoring. IEEE Transactions on Intelligent Transportation Systems. 2019; 20 (7):2681-2690.
Chicago/Turabian StyleTao Wen; Deyi Dong; Qianyu Chen; Lei Chen; Clive Roberts. 2019. "Maximal Information Coefficient-Based Two-Stage Feature Selection Method for Railway Condition Monitoring." IEEE Transactions on Intelligent Transportation Systems 20, no. 7: 2681-2690.
The demands for reliable, well-maintained rolling stock systems have been increasing. Automation has become the smarter solution in various railway applications to enhance the capacity, reliability, and efficiency of the railway system. Automation systems such as visual inspection and industrial robot arms play a critical role in heavy manipulation and dangerous operations. Maintenance efficiency and overall safety would be greatly improved. There is a research gap for systematic approaches to making decisions on which certain maintenance tasks are suitable for automation. This paper presents a generic railway maintenance function allocation framework, which helps decision making in allocating maintenance tasks at the early stages of introducing automation solutions into modern rolling stock maintenance depot. Depot working environments are analysed and an example of robotic wheelset inspection is presented.
Fan Yang; Edward Stewart; Clive Roberts. A Framework for Railway Maintenance Function Allocation. 2018 International Conference on Intelligent Rail Transportation (ICIRT) 2018, 1 -5.
AMA StyleFan Yang, Edward Stewart, Clive Roberts. A Framework for Railway Maintenance Function Allocation. 2018 International Conference on Intelligent Rail Transportation (ICIRT). 2018; ():1-5.
Chicago/Turabian StyleFan Yang; Edward Stewart; Clive Roberts. 2018. "A Framework for Railway Maintenance Function Allocation." 2018 International Conference on Intelligent Rail Transportation (ICIRT) , no. : 1-5.
Multi-agent system is an interdisciplinary field in modern era which can solve large and complex problems in distributed way. Within a multi-agent system, every agent is able to adapt the dynamic of environment and makes actions together with other agents to achieve a global goal. As a result, multi-agent system attracted lots of attention is different research areas. In this paper, we are proposing a novel multi-agent system for railway traffic management problems, and a series of minor collaborative strategies on decision making for large scale railway network.
Jin Liu; Lei Chen; Clive Roberts; Zhu Li; Tao Wen. A Multi-agent Based Approach for Railway Traffic Management Problems. 2018 International Conference on Intelligent Rail Transportation (ICIRT) 2018, 1 -5.
AMA StyleJin Liu, Lei Chen, Clive Roberts, Zhu Li, Tao Wen. A Multi-agent Based Approach for Railway Traffic Management Problems. 2018 International Conference on Intelligent Rail Transportation (ICIRT). 2018; ():1-5.
Chicago/Turabian StyleJin Liu; Lei Chen; Clive Roberts; Zhu Li; Tao Wen. 2018. "A Multi-agent Based Approach for Railway Traffic Management Problems." 2018 International Conference on Intelligent Rail Transportation (ICIRT) , no. : 1-5.
Demand for passenger and freight railway services has increased in recent years. This puts the railway system under pressure to maximize the use of available capacity. An intelligent traffic management systems (TMS) is used to improve railway performance and handle the railway traffic once a disturbance happens. TMS needs a variety of input data to monitor the current traffic, predict the future traffic and solve potential conflicts. A fundamental input parameter of an intelligent TMS is the position of trains in real-time. Train position reporting inaccuracies can significantly influence the TMS performance and lead it to provide suboptimal solutions. In this paper, we assess the impact of position reporting inaccuracies on a local TMS applied to timetables consuming different amounts of capacity. A case study on Dundee Central Junction, in the Scotland/UK, has been investigated. The results are compared using the trains' total delay and the number of cases where TMS has been misguided and a suboptimal solution was produced. The results show that, once TMS is misguided by positioning inaccuracies, a high capacity timetable's TMS solution leads to more delays compared with a lower capacity timetable.
Hassan Hamid; Gemma L. Nicholson; Clive Roberts. An Investigation into the Impact of Positioning System Inaccuracies with a Traffic Management System on Line Capacity. 2018 International Conference on Intelligent Rail Transportation (ICIRT) 2018, 1 -5.
AMA StyleHassan Hamid, Gemma L. Nicholson, Clive Roberts. An Investigation into the Impact of Positioning System Inaccuracies with a Traffic Management System on Line Capacity. 2018 International Conference on Intelligent Rail Transportation (ICIRT). 2018; ():1-5.
Chicago/Turabian StyleHassan Hamid; Gemma L. Nicholson; Clive Roberts. 2018. "An Investigation into the Impact of Positioning System Inaccuracies with a Traffic Management System on Line Capacity." 2018 International Conference on Intelligent Rail Transportation (ICIRT) , no. : 1-5.