[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. (Author)

[en] New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers. (Author)

[en] The paper presents an overview on the road safety measures implemented in the framework of the “SOL – Save our lives” project. It contains summarization of general knowledge regarding the efficiency of the measures applied and conclusions from the analyses of developed strategies and action plans, including common issues, strengths and weaknesses of developed tools and puts these in the context of wider European Road Safety strategies. The purpose of the paper is to provide recommendations for an effective professional development of road safety programs at community level in the context of sustainable mobility. (Author)

[en] The importance of PLC testing has increased in the nuclear I and C domain. While regulation authorities require both functional and structural testing for safety system software, FBD testing relies only on functional testing and there has been little research on structural testing techniques for FBD programs. We aim to analyze current techniques related to FBD testing and develop a structural testing technique appropriate to FBD programs. We developed structural test coverage criteria applicable to FBD programs, focusing on data paths from input edges to output edges of FBD programs. A data path condition (DPC), under which input data can flow into the output edge, is defined for each data path. We defined basic coverage, input condition coverage and complex condition coverage criteria based on the formal definition of DPC. We also developed a measurement procedure for FBD testing adequacy and a supporting tool prototype

[en] This paper introduces the concept of safety-related (SR) uncertainty and the methodology to measure SR uncertainty. SR uncertainty is concerned with the effect of parameter uncertainty on the uncertainty of system unsafety (defined with respect to achieve safety integrity level), which is in direct contrast to the effect on overall system uncertainty. The properties of SR uncertainty are discussed and its significance in analyzing safety systems is highlighted. The conventional global sensitivity analysis (GSA) to handle overall uncertainty is inappropriate when SR uncertainty is of interest. We present and discuss four methods to measure SR uncertainty. Three examples are used to demonstrate the effectiveness of the proposed methods in comparison with GSA.

[en] The current practice for assessing driver competence performance is in-cab riding by driver managers. However, this paper investigates whether real-world driving data extracted from on-train monitoring recorders data (OTMR) can be used to assess the driver performance. A number of indicators were used to evaluate the drivers’ performance. These include: their use of the emergency bypass switch, the driver's reminder appliance as well as the driver’s reaction time. A study case illustrated the applicability of OTMR data to estimate the proposed indicators, which suggests that the indicators can be useful in the driver management system in addition to the current indicators. Furthermore, the proposed indicators could be used to tailor the driver training schemes up to their individual needs and evaluate their effectiveness. They could even be used for improving driver competence performance and reducing crash involvement by revealing potentially detrimental driving performance. (Author)

[en] An aftershock warning system (AWS) is a real-time warning system that is deployed immediately after a large damaging earthquake in the epicentral region of the main shock. The primary purpose of such a system is to warn rescue teams and workers within damaged structures of imminent destructive shaking. The authors have examined the utility of such a system (1) by evaluating historical data, and (2) by developing and testing a prototype system during the 1992 Landers, California, aftershock sequence. Analyzing historical data is important in determining when and where damaging aftershocks are likely to occur and the probable usefulness of an AWS in a particular region. As part of this study, they analyzed the spatial and temporal distribution of large (magnitude >5.0) aftershocks from earthquakes with magnitudes >6.0 that took place between 1942 and 1991 in California and Nevada. They found that one-quarter of these large aftershocks occurred from 2 days-2 months after the main event, nearly one-half occurred within the first two days of the main event, and greater than one-half occurred within 20 km of the main shock's epicenter. They also reviewed a case study of the 1985 Mexico City earthquake, which showed that an AWS could have given Mexico City a warning of ∼60 sec before the magnitude 7.6 aftershock that occurred 36 hr. after the main event. They deployed a four-station prototype AWS near Landers after a magnitude 7.4 earthquake occurred on June 28, 1992. The aftershock data, collected from July 3-10, showed that the aftershocks in the vicinity of the four stations varied in magnitude from 3.0-4.4. Using a two-station detection criterion to minimize false alarms, this AWS reliably discriminated between smaller and larger aftershocks within 3 sec of the origin time of the events. This prototype could have provided 6 sec of warning to Palm Springs and 20 sec of warning to San Bernardino of aftershocks occurring in the main-shock epicentral region

[en] This document identifies equipment related to facility safety as required by WHC-CM-1-3, MRP 5.45. This document identifies the Safety Equipment for the Waste Sampling and Characterization Facility complex in accordance with MRP 5.46. The WSCF Final Safety Analysis Report (FSAR), WHC-SD-W011H-SAR-001, establishes the WSCF as a open-quotes Low Hazard Nuclear Facility.close quotes There are no Safety Class items as identified in DOE order 6430.1a. The maximum Safety Class established by the FSAR is Safety Class 3

[en] For the case where trains or channels of standby safety systems consisting of more than two redundant components are tested in a staggered manner, the standby safety components within a train can be tested simultaneously or consecutively. In this case, mixed testing strategies, staggered and non-staggered testing strategies, are used for testing the components. The objective of the present paper is to derive approximate formulas for the estimations of CCF probabilities of the components under mixed testing strategies, based on the basic parameter method. The derived formulas were applied to four redundant check valves to demonstrate their appropriateness

[en] A reliability model for the Thomas Jefferson National Accelerator Facility (formerly CEBAF) personnel safety system has been developed. The model, which was implemented using an Excel spreadsheet, allows simulation of all or parts of the system. Modularity os the model's implementation allows rapid open-quotes what if open-quotes case studies to simulate change in safety system parameters such as redundancy, diversity, and failure rates. Particular emphasis is given to the prediction of failure modes which would result in the failure of both of the redundant safety interlock systems. In addition to the calculation of the predicted reliability of the safety system, the model also calculates availability of the same system. Such calculations allow the user to make tradeoff studies between reliability and availability, and to target resources to improving those parts of the system which would most benefit from redesign or upgrade. The model includes calculated, manufacturer's data, and Jefferson Lab field data. This paper describes the model, methods used, and comparison of calculated to actual data for the Jefferson Lab personnel safety system. Examples are given to illustrate the model's utility and ease of use