Low adhesion detection and identification in a railway vehicle system using traction motor behaviour

It is important to monitor the wheel-rail friction coefficient in railway vehicles to improve their traction and braking performance as well as to reduce the number of incidents caused by low friction. Model based fault detection and identification (FDI) methods, especially state observers have been commonly used in previous research to monitor the wheel-rail friction. However, the previous methods cannot provide an accurate value of the friction coefficient and few of them have been validated using experiments. A Kalman filter based estimator is proposed in this research project. The developed estimator uses signals from the traction motor and provides a new and more efficient approach to monitoring the condition of the wheel-rail contact condition. A 1/5 scaled test rig has been built to evaluate the developed method. This rig comprises 2 axle-hung induction motors driving both the wheelsets of the bogie through 2 pairs of spur gears. 2 DC generators are used to provide traction load to the rollers through timing pulleys. The motors are independently controlled by 2 inverters. Motor parameters such as voltage, current and speed are measured by the inverters. The speed of the wheel and roller and the output of the DC generator are measured by incremental encoders and Hall-effect current clamps. A LabVIEW code has been designed to process all the collected data and send control commands to the inverters. The communication between the PC and the inverters are realized using the Profibus (Process Field Bus) and the OPC (Object Linking and Embedding (OLE) for Process Control) protocol. 3 different estimators were first developed using computer simulations. Kalman filter and its two nonlinear developments: extended Kalman filter (EKF) and unscented Kalman filter (UKF) have been used in these 3 methods. The results show that the UKF based estimator can provide the best performance in this case. The requirement for measuring the roller speed and the traction load are also studied using the UKF. The results show that it is essential to measure the roller speed but the absence of the traction load measurement does not have significant impact on the estimation accuracy. A re-adhesion control algorithm, which reduces excessive creepage between the wheel and rail, is developed based on the UKF estimator. Accurate monitoring of the friction coefficient helps the traction motor work at its optimum point. As the largest creep force is generated, the braking and accelerating time and distance can be reduced to their minimum values. This controller can also avoid excessive creepage and hence potentially reduce the wear of the wheel and rail. The UKF based estimator development has been evaluated by experiments conducted on the roller rig. Three different friction conditions were tested: base condition without contamination, water contamination and oil contamination. The traction load was varied to cover a large range of creepage. The importance of measuring the roller speed and the traction load was also studied. The UKF based estimator was shown to provide reliable estimation in most of the tested conditions. The experiments also confirm that it is not necessary to measure the traction load and give good agreement with the simulation results. With both the simulation and experiment work, the UKF based estimator has shown its capability of monitoring the wheel-rail friction coefficient

Improving the understanding of SPAD risks using red aspect approach data

This paper describes a novel technique for estimating the frequency with which trains approach signals showing a red aspect. This knowledge is potentially important for understanding the likelihood of a signal being passed at danger (SPAD) at individual signals and also for normalisation of SPAD data, both locally and nationally, for trending and benchmarking. The industry currently uses estimates for the number of red aspect approaches based on driver surveys, which are considered to have significant shortcomings. Data for this analysis is sourced from publicly available live feeds provided by Network Rail which give information on train movements and signal states. The development of the analysis model is described and a case study presented. The case proves that there are large variations in the red approach rates between individual signals. SPAD risk assessment at individual signals may be significantly enhanced by the ability to estimate red approach rates for individual signals using the techniques describe

A validation study of ACS-SSI for online condition monitoring of vehicle suspension systems

Condition monitoring (CM) is an effective approach to prevent accidents caused by structural damage. An online condition monitoring system for suspension is vital to vehicles’ safety and reliability, as suspension is an important subsystem of the vehicle. Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) is a new approach which has the potential to achieve online CM for vehicle suspension systems. In order to investigate the influences of possible errors, like placement of sensors and excitation amplitudes, on implementing ACS-SSI for online suspension CM, a simplified test device is developed to study the performance of identifying the most three common vibration modes of a vehicle, which are bounce, pitch and roll. A three degrees of freedom (3-DOF) model were established for the devices to highlight the effects of the errors. The study results show that the ACS-SSI is an effective method to carry out system identification even if the inputs are highly noisy and non-stationary. However, the implementation of ACS-SSI needs to take into account these potential errors in order to obtain accurate CM result

Power regeneration in the primary suspension of a railway vehicle

This paper presents an assessment of the potential for the use of power regenerating dampers (PRDs) in railway vehicle primary suspension systems equipped with the ‘Hybrid Mode’ and ‘Replace Mode’, and the evaluation of the potential/recoverable power that can be obtained. The power regenerating damper is configured as a hydraulic-electromagnetic based damper. Implications for ride comfort and running safety are also commented for investigating the performance of the suspension system. Several case studies of generic railway vehicle primary suspension systems are modelled and configured to include a power regenerating damper with two different configuration modes. Simulations are then carried out on track with typical irregularities for a generic UK passenger vehicle. The performance of the modified vehicle including regenerated power, ride comfort and running safety is evaluated. Analysis of key influencing factors are also carried out to examine their effects on power capability, ride comfort and running safety to guide the primary suspension design/specification

Improving multi-hop knowledge base question answering by learning intermediate supervision signals

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding InitiativeThe code is available at https://github.com/RichardHGL/WSDM2021_NSM</p

Complex Knowledge Base Question Answering: A Survey

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performance on complex questions is still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances on KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we describe benchmark datasets for complex KBQA task and introduce the construction process of these datasets. Next, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their major differences and similarities. After that, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions and techniques used in existing work. Finally, we conclude and discuss several promising directions related to complex KBQA for future research.Comment: 20 pages, 4 tables, 7 figures. arXiv admin note: text overlap with arXiv:2105.1164

Estimating the frequency of trains approaching red signals: A case study for improving the understanding of SPAD risk

This paper describes a novel technique for estimating the frequency with which trains approach signals showing a red aspect. This knowledge is potentially important for understanding the likelihood of a signal being passed at danger (SPAD) at individual signals and also for normalisation of SPAD data, both locally and nationally, for trending and benchmarking. The industry currently uses estimates for the number of red aspect approaches based on driver surveys which are considered to have significant shortcomings. Data for this analysis is sourced from publicly available live feeds provided by Network Rail which give information on train movements and signal states. The development of the analysis model and supporting software are described and some sample results from case studies are presented. An initial study of seven signalling areas showed that approximately 3.3% of all signal approaches are to red signals. However, it also highlighted that there is a large variation in the red approach rates between signalling areas and between individual signals. SPAD risk assessment at individual signals may be significantly enhanced by the ability to estimate red approach rates for individual signals using the techniques described