Clive Roberts

Single-Train Trajectory Optimization

An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.

Time series methods applied to failure prediction and detection

Point mechanisms are critical track elements on railway networks. A failure in a single point mechanism causes delays, increased railway operating costs and even fatal accidents. This paper describes the development of a new robust and automatic algorithm for failure detection of point mechanisms. Failures are detected by comparing what can be considered the ‘expected’ form of signals predicted from historical records of point mechanism operation with those actually measured. The expected shape is a forecast from a combination of a VARMA (vector auto-regressive moving-average) model and a harmonic regression model. The algorithm has been tested on a large dataset taken from an in-service point mechanism at Abbotswood Junction in the UK. The results show that the faults can be predicted and detected.

Reliability evaluation and optimisation of imperfect inspections for a component with multi-defects

In this paper, a model is developed to evaluate the reliability and optimise the inspection schedule for a multi-defect component. The model uses a non-homogeneous Poisson process (NHPP) method in conjunction with a delay time approach. The inspections are designed to detect any defects in the component, however it can be imperfect. The defect is a definable state before a functional failure happens to the component. Occurrences of defects are assumed to follow an NHPP and a defect will be minimally repaired if it is identified during an inspection. It is shown that the failures occurring in an interval of inspection will also follow an NHPP. The situation of imperfect inspections and non-constant inspection intervals are considered. An algorithm is presented to optimise the intervals of inspections in order to maximise the reliability of the component, and the properties of the algorithm are shown. A numerical example with parametric study is given to show the performance of the model and the algorithm.

Increasing the Regenerative Braking Energy for Railway Vehicles

Regenerative braking improves the energy efficiency of railway transportation by converting kinetic energy into electric energy. This paper proposes a method to apply the Bellman-Ford (BF) algorithm to search for the train braking speed trajectory to increase the total regenerative braking energy (RBE) in a blended braking mode with both electric and mechanical braking forces available. The BF algorithm is applied in a discretized train-state model. A typical suburban train has been modeled and studied under real engineering scenarios involving changing gradients, journey time, and speed limits. It is found that the searched braking speed trajectory is able to achieve a significant increase in the RBE, in comparison with the constant-braking-rate (CBR) method with only a minor difference in the total braking time. An RBE increment rate of 17.23% has been achieved. Verification of the proposed method using BF has been performed in a simplified scenario with zero gradient and without considering the constraints of braking time and speed limits. Linear programming (LP) is applied to search for a train trajectory with the maximum RBE and achieves solutions that can be used to verify the proposed method using BF. It is found that it is possible to achieve a near-optimal solution using BF and the solution can be further improved with a more complex search space. The proposed method takes advantage of robustness and simplicity of modeling in a complex engineering scenario, in which a number of nonlinear constraints are involved.

A Multiple Train Trajectory Optimization to Minimize Energy Consumption and Delay

In railway operations, if the journey of a preceding train is disturbed, the service interval between it and the following trains may fall below the minimum line headway distance. If this occurs, train interactions will happen, which will result in extra energy usage, knock-on delays, and penalties for the operators. This paper describes a train trajectory (driving speed curve) optimization study to consider the tradeoff between reductions in train energy usage against increases in delay penalty in a delay situation with a fixed block signaling system. The interactions between trains are considered by recalculating the behavior of the second and subsequent trains based on the performance of all trains in the network, apart from the leading train. A multitrain simulator was developed specifically for the study. Three searching methods, namely, enhanced brute force, ant colony optimization, and genetic algorithm, are implemented in order to find the optimal results quickly and efficiently. The result shows that, by using optimal train trajectories and driving styles, interactions between trains can be reduced, thereby improving performance and reducing the energy required. This also has the effect of improving safety and passenger comfort.

A Cooperative Train Control Model for Energy Saving

Increasing attention is being paid to energy efficiency in subway systems to reduce operational cost and carbon emissions. Optimization of the driving strategy and efficient utilization of regenerative energy are two effective methods to reduce the energy consumption for electric subway systems. Based on a common scenario that an accelerating train can reuse the regenerative energy from a braking train on the opposite track, this paper proposes a cooperative train control model to minimize the practical energy consumption, i.e., the difference between traction energy and the reused regenerative energy. First, we design a numerical algorithm to calculate the optimal driving strategy with the given trip time, in which the variable traction force, braking force, speed limits, and gradients are considered. Then, a cooperative train control model is formulated to adjust the departure time of the accelerating train for reducing the practical energy consumption during the trip by efficiently using the regenerative energy of the braking train. Furthermore, a bisection method is presented to solve the optimal departure time for an accelerating train. Finally, the optimal driving strategy is obtained for the accelerating train with the optimal departure time. Case studies based on the Yizhuang Line, Beijing Subway, China, are presented to illustrate the effectiveness of the proposed approach on energy saving.

Railway Point Mechanisms: Condition Monitoring and Fault Detection

Early attempts at monitoring the condition of railway point mechanisms employed simple thresholding techniques to detect faults, but success was limited and there were large numbers of false alarms and missed failures in the field. More recent research using data collected from line-side equipment and lab-based test rigs, though, is suggesting that it should indeed be possible to predict failures with sufficient accuracy and notice to be of genuine use to infrastructure maintainers and owners. This review into state-of-the-art predictive fault detection and diagnosis methods shows how some very different generic models have been tailored to the various types of mechanisms that are in use worldwide. In any specific case, the most appropriate combination of quantitative and qualitative techniques will be determined by the inherent failure modes of the system and the particular conditions under which it operates. Furthermore, it is vital to have a priori knowledge of the symptoms that are observable under fault conditions if diagnosis is to be reliable.

Perspectives on railway track geometry condition monitoring from in-service railway vehicles

This paper presents a view of the current state of monitoring track geometry condition from in-service vehicles. It considers technology used to provide condition monitoring; some issues of processing and the determination of location; how things have evolved over the past decade; and what is being, or could/should be done in future research. Monitoring railway track geometry from an in-service vehicle is an attractive proposition that has become a reality in the past decade. However, this is only the beginning. Seeing the same track over and over again provides an opportunity for observing track geometry degradation that can potentially be used to inform maintenance decisions. Furthermore, it is possible to extend the use of track condition information to identify if maintenance is effective, and to monitor the degradation of individual faults such as dipped joints. There are full unattended track geometry measurement systems running on in-service vehicles in the UK and elsewhere around the world, feeding their geometry measurements into large databases. These data can be retrieved, but little is currently done with the data other than the generation of reports of track geometry that exceeds predefined thresholds. There are examples of simpler systems that measure some track geometry parameters more or less directly and accurately, but forego parameters such as gauge. Additionally, there are experimental systems that use mathematics and models to infer track geometry using data from sensors placed on an in-service vehicle. Finally, there are systems that do not claim to measure track geometry, but monitor some other quantity such as ride quality or bogie acceleration to infer poor track geometry without explicitly measuring it.

Assessing the Economic Life of Rail Using a Stochastic Analysis of Failures

Abstract In this article, a model is developed to evaluate the economic life of rail using a stochastic analysis of rail failures. The occurrence of rail defects and failures is analysed and issues related to rail maintenance are addressed. The hazard rate of alumino-thermic weld failures is predicted for continuously welded rail, where the expected number of welds is increasing in tonnage as a result of replacement. Imperfect inspections are modelled for predicting the expected number of failures by employing a filtered non-homogeneous Poisson process. The impact of maintenance on failure occurrence and whole life-cycle cost (LCC) is analysed and integrated within the model. The application and capability of the proposed model and sensitivity of parameters are illustrated through the use of a practical example. It is shown that an optimal economic life of rail can always be found, as the repair cost and the risk cost of accident are increasing with tonnage that passed through it. In addition, the inspection frequency has a significant impact on rail LCC. The results also suggest that the proposed model is also valid for optimizing the interval between rail inspections.

New methods for the condition monitoring of level crossings

Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motors current and voltage, hydraulic pressure and the barriers position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the projects proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a ‘normal’ or ‘expected’ shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque–Bera and Ljung–Box have been considered for testing the model.