Derailment risk analysis, monitoring and management at railway turnouts

The general objective of the thesis is to develop a number of novel Bayesian- based mathematical models that are applicable for the railway sector. Hence, it is assumed that the thesis will be an element of, or facilitate future AI (Artificial Intelligence) Risk Management and Safety Standards, which will inevitably be developed for the sector. The thesis primarily concentrates on applications that support decision-making processes, related to derailments at railway turnout sys- tem. The first objective is to determine, evaluate and prioritise the risk factors that cause derailments; secondly, it will identify and demonstrate the relationship among these driving factors; and finally, it will show the prospective usage of Bayesian networks as an intuitive modelling instrument that makes the process of modelling risk more transparent and consistent. In order to achieve the aforementioned objectives, this thesis is established on various novel methodological approaches using either qualitative or quantitative methods, or a combination of the two. A comprehensive review is conducted in order to interpret and acquire an in-depth understanding of suitable methods of analysing risk in addition to five original studies on the subjects of component failures, human errors and the environmental impact to measure, rank, categorise, and identify the factors that cause derailment in the railway sector. The proposed novel methodologies in addition to their MATLAB and R codes are introduced for utilisation in a developed framework for analysing, monitoring and managing risk for railway turnout

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation

Sample adaptive multiple kernel learning for failure prediction of railway points

© 2019 Association for Computing Machinery. Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Meanwhile, they are also one of the most fragile parts in railway systems. Points failures cause a large portion of railway incidents. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, min-imising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorithm compared with other state-of-the-art methods

An Analysis of Track Geometry Data in Combination with Supporting Exogenous Sources Using Linear Regression Techniques

We have investigated relationships between track geometry and weather, to determine if weather has any effect on degradation of track. The data provides an appropriate testbed, covering two Engineers’ Line Reference (ELR) IDs of known geological differences, allowing us to explore how weather affects different areas of the railway. We have justified the decision to exploit linear regression modelling, in order to provide a preliminary analysis as the basis for future work. As such, we process and develop a feature table from raw track geometry data that details the track geometry in 200m sections, named Location IDs. From this data, we have applied single and multivariate linear regression models to the dataset and provided an array of visualisations and supporting data. We confirm that linear regression was a suitable investigatory technique, supplying R2 values of up to 69.7%

A failure probability assessment method for train derailments in railway yards based on IFFTA and NGBN

Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards

Use of DELPHI-AHP Method to Identify annd Analyze Risks in Seaport Dry Port System

The dry port concept has recently gained rising consideration in the multimodal transport context from the point of view both of researchers and stakeholders related to the benefits of the seaport dry port system. Given the relevance of the topic, the present paper aims to identify the potential risk factors of the three major parts that constitute the seaport dry port system and present a conceptual framework to facilitate risk factors analysis. Based on a three-step approach, starting with a systematic literature review, which resulted in 204 collected and examined papers, which allowed identifying 181 potential risk factors with an average of 60 risk factors in each major part of the studied system. In addition, we used a survey based on the Delphi technique to ensure a good extraction of data from 12 selected experts related to the seaport dry port system; then, we used the MCDM (Multiple-Criteria Decision-Making) method AHP (Analytic Hierarchy Process) in order to: 1) present a hierarchy that simplifies the complexity of the studied system in an organized structure; 2) analyze and assess risk factors based on the identified criteria. A case study involving the Moroccan seaport dry port system of Casablanca illustrates that the seaport part is critical and any major risk factor in this part can even paralyze the operations of the whole system, especially if that risk factor belongs to the human factors category or economic risk category, which is also considered in the study as a critical category

Learning from accidents : machine learning for safety at railway stations

In railway systems, station safety is a critical aspect of the overall structure, and yet, accidents at stations still occur. It is time to learn from these errors and improve conventional methods by utilizing the latest technology, such as machine learning (ML), to analyse accidents and enhance safety systems. ML has been employed in many fields, including engineering systems, and it interacts with us throughout our daily lives. Thus, we must consider the available technology in general and ML in particular in the context of safety in the railway industry. This paper explores the employment of the decision tree (DT) method in safety classification and the analysis of accidents at railway stations to predict the traits of passengers affected by accidents. The critical contribution of this study is the presentation of ML and an explanation of how this technique is applied for ensuring safety, utilizing automated processes, and gaining benefits from this powerful technology. To apply and explore this method, a case study has been selected that focuses on the fatalities caused by accidents at railway stations. An analysis of some of these fatal accidents as reported by the Rail Safety and Standards Board (RSSB) is performed and presented in this paper to provide a broader summary of the application of supervised ML for improving safety at railway stations. Finally, this research shows the vast potential of the innovative application of ML in safety analysis for the railway industry

On Analysis of the Predictive Maintenance of Railway Points Processes and Possibilities

A railway network is vital condition for a blooming industry and fluent public transportation in most countries. To maintain safety and fluency in the traffic it needs to be constantly repaired. A pivotal part of the network -- railway points and their maintenance actions, is heavily regulated, leading to periodical visits to the points. However, these visit do not prevent all failures in the railway points and additionally are very costly. Scientists are constantly seeking possibilities to achieve condition-based regulation. However, all the reasonable approaches studied require both a lot of investments in additional equipment and the co-operation of several companies (those responsible for different aspects of the network). Recently cloud-based digitalisation in the railway industry has made multi company co-operation more practical and brought possibilities for data analysis. This thesis describes three aspects that are necessary to gain more from digitalisation in this field. First, principles and challenges of data analysis project. Secondly thesis investigates feasibility of railway point failures prediction between periodical maintenance visits with existing data. Thirdly, company co-operation requirements regarding data quality, and the formats of signalling logs and maintenance reports. The emphasis is on feasibility analysis and, with the help of typical machine learning algorithms, this thesis shows that there is potential to improve maintenance planning with existing data. However, the prediction accuracies achieved in the thesis indicates that without investing in additional equipment or more precise log measures, the accuracies are not in correct level to start processes towards condition-based regulation