Probabilistic Safety Analysis of High Speed and Conventional Lines Using Bayesian Networks

[EN] A Bayesian network approach is presented for probabilistic safety analysis (PSA) of railway lines. The idea consists of identifying and reproducing all the elements that the train encounters when circulating along a railway line, such as light and speed limit signals, tunnel or viaduct entries or exits, cuttings and embankments, acoustic sounds received in the cabin, curves, switches, etc. In addition, since the human error is very relevant for safety evaluation, the automatic train protection (ATP) systems and the driver behavior and its time evolution are modelled and taken into account to determine the probabilities of human errors. The nodes of the Bayesian network, their links and the associated probability tables are automatically constructed based on the line data that need to be carefully given. The conditional probability tables are reproduced by closed formulas, which facilitate the modelling and the sensitivity analysis. A sorted list of the most dangerous elements in the line is obtained, which permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs substantially. The proposed methodology is illustrated by its application to several cases that include real lines such as the Palencia-Santander and the Dublin-Belfast lines.Grande Andrade, Z.; Castillo Ron, E.; Nogal, M.; O'connor, A. (2016). Probabilistic Safety Analysis of High Speed and Conventional Lines Using Bayesian Networks. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 362-371. https://doi.org/10.4995/CIT2016.2015.3428OCS36237

Using Bayesian networks to represent parameterised risk models for the UK railways

PhDThe techniques currently used to model risk and manage the safety of the UK railway network are not aligned to the mechanism by which catastrophic accidents occur in this industry. In this thesis, a new risk modelling method is proposed to resolve this problem. Catastrophic accidents can occur as the result of multiple failures occurring to all of the various defences put in place to prevent them. The UK railway industry is prone to this mechanism of accident occurrence, as many different technical, operational and organizational defences are used to prevent accidents. The railway network exists over a wide geographic area, with similar accidents possible at many different locations. The risk from these accidents is extremely variable and depends on the underlying conditions at each particular location, such as the state of assets or the speed of trains. When unfavourable conditions coincide the probability of multiple failures of planned defences increases and a 'risk hotspot' arises. Ideal requirements for modelling risk are proposed, taking account of the need to manage multiple defences of conceptually different type and the existence of risk hotspots. The requirements are not met by current risk modelling techniques although some of the requirements have been addressed experimentally, and in other industries and countries. It is proposed to meet these requirements using Bayesian Networks to supplement and extend fault and event tree analysis, the traditional techniques used for risk modelling in the UK railway industry. Application of the method is demonstrated using a case study: the building of a model of derailment risk on the UK railway network. The proposed method provides a means of better integrating industry wide analysis and risk modelling with the safety management tasks and safety related decisions that are undertaken by safety managers in the industry

A Bayesian Network methodology for railway risk, safety and decision support

For railways, risk analysis is carried out to identify hazardous situations and their consequences. Until recently, classical methods such as Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in modelling the linear and logically deterministic aspects of railway risks, safety and reliability. However, it has been proven that modern railway systems are rather complex, involving multi-dependencies between system variables and uncertainties about these dependencies. For train derailment accidents, for instance, high train speed is a common cause of failure; slip and failure of brake applications are disjoint events; failure dependency exists between the train protection and warning system and driver errors; driver errors are time dependent and there is functional uncertainty in derailment conditions. Failing to incorporate these aspects of a complex system leads to wrong estimations of the risks and safety, and, consequently, to wrong management decisions. Furthermore, a complex railway system integrates various technologies and is operated in an environment where the behaviour and failure modes of the system are difficult to model using probabilistic techniques. Modelling and quantification of the railway risk and safety problems that involve dependencies and uncertainties such as mentioned above are complex tasks. Importance measures are useful in the ranking of components, which are significant with respect to the risk, safety and reliability of a railway system. The computation of importance measures using FTA has limitation for complex railways. ALARP (As Low as Reasonably Possible) risk acceptance criteria are widely accepted as ’\'best practice’’ in the railways. According to the ALARP approach, a tolerable region exists between the regions of intolerable and negligible risks. In the tolerable region, risk is undertaken only if a benefit is desired. In this case, one needs to have additional criteria to identify the socio-economic benefits of adopting a safety measure for railway facilities. The Life Quality Index (LQI) is a rational way of establishing a relation between the financial resources utilized to improve the safety of an engineering system and the potential fatalities that can be avoided by safety improvement. This thesis shows the application of the LQI approach to quantifying the social benefits of a number of safety management plans for a railway facility. We apply Bayesian Networks and influence diagrams, which are extensions of Bayesian Networks, to model and assess the life safety risks associated with railways. Bayesian Networks are directed acyclic probabilistic graphical models that handle the joint distribution of random variables in a compact and flexible way. In influence diagrams, problems of probabilistic inference and decision making – based on utility functions – can be combined and optimized, especially, for systems with many dependencies and uncertainties. The optimal decision, which maximizes the total benefits to society, is obtained. In this thesis, the application of Bayesian Networks to the railway industry is investigated for the purpose of improving modelling and the analysis of risk, safety and reliability in railways. One example application and two real world applications are presented to show the usefulness and suitability of the Bayesian Networks for the quantitative risk assessment and risk-based decision support in reference to railways.:ACKNOWLEDGEMENTS IV ABSTRACT VI ZUSAMMENFASSUNG VIII LIST OF FIGURES XIV LIST OF TABLES XVI CHAPTER 1: Introduction 1 1.1 Need to model and quantify the causes and consequences of hazards on railways 1 1.2 State-of-the art techniques in the railway 2 1.3 Goals and scope of work 4 1.4 Existing work 6 1.5 Outline of the thesis 7 CHAPTER 2: Methods for safety and risk analysis 10 2.1 Introduction 10 2.1.1 Simplified risk analysis 12 2.1.2 Standard risk analysis 12 2.1.3 Model-based risk analysis 12 2.2 Risk Matrix 14 2.2.1 Determine the possible consequences 14 2.2.2 Likelihood of occurrence 15 2.2.3 Risk scoring matrix 15 2.3 Failure Modes & Effect Analysis – FMEA 16 2.3.1 Example application of FMEA 17 2.4 Fault Tree Analysis – FTA 19 2.5 Reliability Block Diagram – RBD 22 2.6 Event Tree Analysis – ETA 24 2.7 Safety Risk Model – SRM 25 2.8 Markov Model – MM 27 2.9 Quantification of expected values 31 2.9.1 Bayesian Analysis – BA 35 2.9.2 Hazard Function – HF 39 2.9.3 Monte Carlo (MC) Simulation 42 2.10 Summary 46 CHAPTER 3: Introduction to Bayesian Networks 48 3.1 Terminology in Bayesian Networks 48 3.2 Construction of Bayesian Networks 49 3.3 Conditional independence in Bayesian Networks 51 3.4 Joint probability distribution in Bayesian Networks 52 3.5 Probabilistic Inference in Bayesian Networks 53 3.6 Probabilistic inference by enumeration 54 3.7 Probabilistic inference by variable elimination 55 3.8 Approximate inference for Bayesian Networks 57 3.9 Dynamic Bayesian Networks 58 3.10 Influence diagrams (IDs) 60 CHAPTER 4: Risk acceptance criteria and safety targets 62 4.1 Introduction 62 4.2 ALARP (As Low As Reasonably Possible) criteria 62 4.3 MEM (Minimum Endogenous Mortality) criterion 63 4.4 MGS (Mindestens Gleiche Sicherheit) criteria 64 4.5 Safety Integrity Levels (SILs) 65 4.6 Importance Measures (IMs) 66 4.7 Life Quality Index (LQI) 68 4.8 Summary 72 CHAPTER 5: Application of Bayesian Networks to complex railways: A study on derailment accidents 73 5.1 Introduction 73 5.2 Fault Tree Analysis for train derailment due to SPAD 74 5.2.1 Computation of importance measures using FTA 75 5.3 Event Tree Analysis (ETA) 78 5.4 Mapping Fault Tree and Event Tree based risk model to Bayesian Networks 79 5.4.1 Computation of importance measures using Bayesian Networks 81 5.5 Risk quantification 82 5.6 Advanced aspects of example application 83 5.6.1 Advanced aspect 1: Common cause failures 83 5.6.2 Advanced aspect 2: Disjoint events 84 5.6.3 Advanced aspect 3: Multistate system and components 84 5.6.4 Advanced aspect 4: Failure dependency 85 5.6.5 Advanced aspect 5: Time dependencies 85 5.6.6 Advanced aspect 6: Functional uncertainty and factual knowledge 85 5.6.7 Advanced aspect 7: Uncertainty in expert knowledge 86 5.6.8 Advanced aspect 8: Simplifications and dependencies in Event Tree Analysis 86 5.7 Implementation of the advanced aspects of the train derailment model using Bayesian Networks. 88 5.8 Results and discussions 92 5.9 Summary 93 CHAPTER 6: Bayesian Networks for risk-informed safety requirements for platform screen doors in railways 94 6.1 Introduction 94 6.2 Components of the risk-informed safety requirement process for Platform Screen Door system in a mega city 97 6.2.1 Define objective and methodology 97 6.2.2 Familiarization of system and information gathering 97 6.2.3 Hazard identification and hazard classification 97 6.2.4 Hazard scenario analysis 98 6.2.5 Probability of occurrence and failure data 99 6.2.6 Quantification of the risks 105 6.2.6.1. Tolerable risks 105 6.2.6.2. Risk exposure 105 6.2.6.3. Risk assessment 106 6.3 Summary 107 CHAPTER 7: Influence diagrams based decision support for railway level crossings 108 7.1 Introduction 108 7.2 Level crossing accidents in railways 109 7.3 A case study of railway level crossing 110 7.4 Characteristics of the railway level crossing under investigation 111 7.5 Life quality index applied to railway level crossing risk problem 115 7.6 Summary 119 CHAPTER 8: Conclusions and outlook 120 8.1 Summary and important contributions 120 8.2 Originality of the work 122 8.3 Outlook 122 BIBLIOGRAPHY 124 APPENDIX 1 13

Role of Pre-processing in Textual Data Fusion: Learn From the Croydon Tram Tragedy

Tram/train derailment subject to human mistakes makes investments in an advanced control room as well as information gathering system exaggerated. A disaster in Croydon in year 2016 is recent evidence of limitation of the acquired systems to mitigate human shortcoming in disrupted circumstances. One intriguing way of resolution could be is to fuse continuous online textual data obtained from tram travelers and apply the information for early cautioning of risk discovery. This resolution conveys our consideration regarding a resource of data fusion. The focal subject of this paper is to discuss about role of pre-processing ventures in a low-level data fusion that have been distinguished as a pass to avoid time and exertion squandering amid information retrieval. Inclines in online text data pre-processing is reviewed which comes about an outline suggestion that concede traveler's responses through social media channels. The research outcome shows by a case of data fusion could go about as an impetus to railway industry to effectively partake in data exploration and information investigation

Safety analysis of offshore decommissioning operation through Bayesian network

Decommissioning of offshore platforms is becoming increasingly popular. The removal of these heavy steel structures is characterised by high risks that may compromise personnel safety and loss of assets. The removal operation relies on dedicated barges and heavy lift vessels that may descent or capsize because of mechanical or structural failure. The knowledge of associated hazards is driven by experience and failure data are often obtained empirically through analogous operations, which further introduces uncertainty to the risk analysis. This paper proposes an integrated safety analysis approach for conducting a decommissioning risk analysis of offshore installations. The approach incorporates hierarchical Bayesian analysis (HBA) with Bayesian network (BN) to assess the accident causations leading to futile decommissioning operation. First, the overall system failure of a lifting vessel was reviewed with an emphasis on where safety issues arise. In addition, the failure data obtained from expert judgements were aggregated through statistical distribution based on HBA. The aggregated failure data are then used to conduct dynamic safety analysis using BN, to assess and evaluate the risks of offshore jacket removal operations. The accident model is illustrated with a case study from Brent Alpha decommissioning technical document to demonstrate the capability of incorporating HBA with BN to conduct a risk analysis

Research on the System Safety Management in Urban Railway

Nowadays, rail transport has become one of the most widely utilised forms of transport thanks to its high safety level, large capacity, and cost-effectiveness. With the railway network's continuous development, including urban rail transit, one of the major areas of increasing attention and demand is ensuring safety or risk management in operation long-term remains for the whole life cycle by scientific tools, management of railway operation (Martani 2017), specifically in developed and developing countries like Vietnam. The situation in Vietnam demonstrates that the national mainline railway network has been built and operated entirely in a single narrow gauge (1000mm) since the previous century, with very few updates of manual operating technology. This significantly highlights that up to now, the conventional technique for managing the safety operation in general, and collision in particular, of the current Vietnamese railway system, including its subsystems, is only accident statistics which is not a scientific-based tool as the others like risk identify and analyse methods, risk mitigation…, that are already available in many countries. Accident management of Vietnam Railways is limited and responsible for accident statistics analysis to avoid and minimise the harm caused by phenomena that occur only after an accident. Statistical analysis of train accident case studies in Vietnam railway demonstrates that, because hazards and failures that could result in serious system occurrences (accidents and incidents) have not been identified, recorded, and evaluated to conduct safety-driven risk analysis using a well-suited assessment methodology, risk prevention and control cannot be achieved. Not only is it hard to forecast and avoid events, but it may also raise the chance and amount of danger, as well as the severity of the later effects. As a result, Vietnam's railway system has a high number of accidents and failure rates. For example, Vietnam Rail-ways' mainline network accounted for approximately 200 railway accidents in 2018, a 3% increase over the previous year, including 163 collisions between trains and road vehicles/persons, resulting in more than 100 fatalities and more than 150 casualties; 16 accidents, including almost derailments, the signal passed at danger… without fatality or casual-ty, but significant damage to rolling stock and track infrastructure (VR 2021). Focusing and developing a new standardised framework for safety management and availability of railway operation in Vietnam is required in view of the rapid development of rail urban transport in the country in recent years (VmoT 2016; VmoT 2018). UMRT Line HN2A in southwest Hanoi is the country's first elevated light rail transit line, which was completed and officially put into revenue service in November 2021. This greatly highlights that up to the current date, the UMRT Line HN2A is the first and only railway line in Vietnam with operational safety assessment launched for the first time and long-term remains for the whole life cycle. The fact that the UMRT Hanoi has a large capacity, more complicated rolling stock and infrastructure equipment, as well as a modern communica-tion-based train control (CBTC) signalling system and automatic train driving without the need for operator intervention (Lindqvist 2006), are all advantages. Developing a compatible and integrated safety management system (SMS) for adaption to the safety operating requirements of this UMRT is an important major point of concern, and this should be proven. In actuality, the system acceptance and safety certification phase for Metro Line HN2A prolonged up to 2.5 years owing to the identification of difficulties with noncompliance to safety requirements resulting from inadequate SMS documents and risk assessment. These faults and hazards have developed during the manufacturing and execution of the project; it is impossible to go back in time to correct them, and it is also impossible to ignore the project without assuming responsibility for its management. At the time of completion, the HN2A metro line will have required an expenditure of up to $868 million, thus it is vital to create measures to prevent system failure and assure passenger safety. This dissertation has reviewed the methods to solve the aforementioned challenges and presented a solution blueprint to attain the European standard level of system safety in three-phase as in the following: • Phase 1: applicable for lines that are currently in operation, such as Metro Line HN2A. Focused on operational and maintenance procedures, as well as a training plan for railway personnel, in order to enhance human performance. Complete and update the risk assessment framework for Metro Line HN2A. The dissertation's findings are described in these applications. • Phase 2: applicable for lines that are currently in construction and manufacturing, such as Metro Line HN3, Line HN2, HCMC Line 1 and Line 2. Continue refining and enhancing engineering management methods introduced during Phase 1. On the basis of the risk assessment by manufacturers (Line HN3, HCMC Line 2 with European manufacturers) and the risk assessment framework described in Chapter 4, a risk management plan for each line will be developed. Building Accident database for risk assessment research and development. • Phase 3: applicable for lines that are currently in planning. Enhance safety requirements and life-cycle management. Building a proactive Safety Culture step by step for the railway industry. This material is implemented gradually throughout all three phases, beginning with the creation of the concept and concluding with an improvement in the attitude of railway personnel on the HN2A line. In addition to this overview, Chapters 4 through Chapter 9 of the dissertation include particular solutions for Risk assessment, Vehicle and Infrastructure Maintenance methods, Inci-dent Management procedures, and Safety Culture installation. This document focuses on constructing a system safety concept for railway personnel, providing stringent and scientific management practises to assure proper engineering conditions, to manage effectively the metro line system, and ensuring passenger safety in Hanoi's metro operatio

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

Causality and Functional Safety - How Causal Models Relate to the Automotive Standards ISO 26262, ISO/PAS 21448, and UL 4600

With autonomous driving, the system complexity of vehicles will increase drastically. This requires new ap- proaches to ensure system safety. Looking at standards like ISO 26262 or ISO/PAS 21448 and their suggested methodologies, an increasing trend in the recent literature can be noticed to incorporate uncertainty. Often this is done by using Bayesian Networks as a framework to enable probabilistic reasoning. These models can also be used to represent causal relationships. Many publications claim to model cause-effect relations, yet rarely give a formal introduction of the implications and resulting possibilities such an approach may have. This paper aims to link the domains of causal reasoning and automotive system safety by investigating relations between causal models and approaches like FMEA, FTA, or GSN. First, the famous “Ladder of Causation” and its implications on causality are reviewed. Next, we give an informal overview of common hazard and reliability analysis techniques and associate them with probabilistic models. Finally, we analyse a mixed-model methodology called Hybrid Causal Logic, extend its idea, and build the concept of a causal shell model of automotive system safety