Positioneringssystem har en vital roll när det gäller att kontrollera säkra tågrörelser. Det finnsmånga olika typer av positioneringssystem. Det här arbetet behandlar hur axelräknare, communications-based train control (CBTC) och olika typer av spårledningar fungerar. Det innehåller ocksåen analys av växelströmsspårledningar och axelräknare med RAMS-parametrar (Reliability, Availability,Maintenance and Safety) som utgångspunkt för att dra slutsats om vilken typ som passar bästför Trafikförvaltningens spårburna banor. Genom intervjuer med erfarna personer inom järnvägsbranscheni Stockholm erhölls kunskap om för- och nackdelar med olika system. En felträdsanalys(FTA) utfördes för axelräknare och spårledningar för att åskådliggöra potentiellt farliga situationer.Felstatistik för två järnvägsbanor togs fram för att visa felfrekvens för en bana med axelräknare ochen bana med spårledningar. Resultatet är inte entydigt men visar ändå att axelräknare undvikerfelkällor som finns hos spårledningar. Det som tydligast kommit fram var att förvaltningen behöverstandardisera till färre typer av system för de olika banorna. Det skulle underlätta vid förvärvandetav personal med rätt kompetens som ska utföra underhåll. Det skulle också göra det lättare att säkerställatillgången till ersättningsprodukter. / Positioning systems have a vital role in securing safe movement of trains. There are many differenttypes of positioning systems. This thesis is about how axle counters, communications-based traincontrol (CBTC) and different kinds of track circuits operate. It also contains an analysis of AC-trackcircuits and axle counters with RAMS-parameters (Reliability, Availability, Maintenance and Safety)as guide points to make a conclusion of what type of system that best suits for Stockholm PublicTransports railway tracks. Through interviews with experienced persons within the railway industryin Stockholm knowledge of pros and cons of different systems was obtained. A fault tree analyses(FTA) was made for axle counters and track circuits to visualize potentially hazardous situations.Failure statistics were produced to show failure frequency for one track with axle counters and onetrack with track circuits. A clear result was not shown but it can be concluded that sources of failurethat are prone to track circuit systems can be avoided using axle counters. What became evident isthat the management need to standardize to a fewer amount of different positioning systems. Itwould make it easier to find available personnel with the required skills for doing maintenance. Thiswould also have a benefit when securing maintenance supplies.

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<p>Although traditional hazard analysis techniques, such as failure modes and effect analysis (FMEA), and fault tree analysis (FTA) have been used for a long time, they are not well-suited to handling modern systems with complex software, human-machine interactions, and decision-making procedures. This is mainly because traditional hazard analysis techniques rely on a direct cause-effect chain and have no unified guidance to lead the hazard analysis. The Systems Theoretic Accident Model and Process (STAMP) is based on systems theory to try to find out as much as possible about the factors involved in a hazard, and with providing clear guidance as to the control structure leading to the hazard.</p> <p>The Darlington Nuclear Power Generating Station was the first nuclear plant in the world in which the safety shutdown systems are computer controlled. Although FTA and FMEA have already been applied to these shutdown systems, Ontario power generation felt that it is still useful to try recent advances to evaluate whether they can improve on the previous hazard analysis.</p> <p>This thesis introduces the two most common traditional techniques of hazard analysis, FTA and FMEA, as well as two systemic techniques, STPA (which is a hazard analysis method associated with STAMP), and the Functional Resonance Accident Model (FRAM). The thesis also explains why we chose STPA to apply to the Darlington Shutdown System case, and provides an example of the application as well as an evaluation of its use compared with FMEA and FTA.</p> / Master of Applied Science (MASc)

Les travaux proposés dans cette thèse s’intéressent à la commande et la surveillance d’une classe particulière de systèmes de production : les systèmes manufacturiers à contraintes de temps de séjour. Nous supposons dans l'étude que les ressources ont déjà été affectées et que l'ordre des opérations est déjà fixé par le module de planification/ordonnancement. Les hypothèses de fonctionnement répétitif avec et sans postes d'assemblage sont adoptées. De manière assez classique pour ce type de problématique, le formalisme utilisé est celui des Réseaux de Petri P-temporels pour l'étude des instants de débuts et de fins des opérations.Une étude de la robustesse des ateliers manufacturiers à contraintes de temps a été développée. La robustesse est abordée avec et sans modification de la commande relative à la robustesse active et à la robustesse passive respectivement, face aux perturbations temporelles. Un algorithme de calcul d'une borne supérieure de la robustesse passive est présenté. De plus, trois stratégies de commande robuste face aux perturbations temporelles ont été développées.Par ailleurs, l’incertitude dans les systèmes de production manufacturière à été étudié. Notre contribution dans ce cadre porte sur l’intégration des résultats concernant la robustesse dans la génération de symptômes et la classification des comptes rendus associés aux différentes opérations en utilisant la logique floue.Partant d’un système commandé, nous avons présenté en détail une démarche à suivre pour la mise en œuvre d’un modèle de surveillance en se basant sur les chroniques et les arbres de défaillance flous. Cette démarche est appliquée à un atelier de production laitière / The works proposed in this thesis are interested in controlling and monitoring of a particular class of production system : manufacturing job-shops with time constraints. We suppose in the study that the resources are allocated and the operations order is fixed by the module of planning/scheduling. The assumptions of repetitive functioning mode with and without assembling tasks are adopted. For this type of problems, the formalism of P-time Petri nets is used in order to study the operations time constraints.A study of the robustness of the manufacturing workshop to time constraints, has been developed. The robustness is approached with and without control reaction qualified as active robustness and passive robustness respectively, towards time disturbances. A computing algorithm of the upper bound of the passive robustness is presented. In addition, three robust control strategies facing time disturbances were developed.Furthermore, uncertainty in manufacturing systems has been studied. Our contribution in this context is by integration of the analytical knowledge of the robustness in the filtering mechanism of sensors signals that are associated to operations, by using fuzzy logic.Starting from a controlled system, we have presented in detail, a method to be followed for the implementation of a monitoring model based on the chronicles and fuzzy fault tree analysis. This approach is applied to a milk production unit

This thesis deals with the matter of risk managament in IT projects. It explains the importance of risk management in such projects and shows different ways and methods of managing and analyzing the risks. After explaining the basic concepts and the various phases of risk management the text focuses on two methods of risk analysis - the fault tree analysis of event tree analysis. Use of both methods is explained for both quantitative and qualitative analyses. The second half of the work includes the design of an application for the support of risk analysis employing the methods of fault tree analysis and event tree analysis. This is followed by a description of the implementation of the proposed system in a web environment using jQuery, Nette Framework and Dibi.

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 1 131