A Situation Analysis Decision Support System Based on Dynamic Object Oriented Bayesian Networks

This paper proposes a situation analysis decision support system (SADSS) for safety of safety-critical systems where the operators are stressed by the task of understanding what is going on in the situation. The proposed SADSS is developed based on a new model-driven engineering approach for hazardous situations modeling based on dynamic object oriented Bayesian networks to reduce the complexity of the decision-making process by aiding operators’ cognitive activities. The SADSS includes four major elements: a situation data collection based on observable variables such as sensors, a situation knowledgebase which consists of dynamic object oriented Bayesian networks to model hazardous situations, a situation analysis which shows the current state of hazardous situations based on risk concept and possible near future state, and a humancomputer interface. Finally two evaluation methods for partial and full validation of SADSS are presented

A situation risk awareness approach for process systems safety

Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today's process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects - addressing hazards that arise from hardware failure and reducing human error through decision-making - have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from US Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach. © 2013 Elsevier Ltd

Risk-based regulation of unmanned aircraft systems

The aviation sector is faced with a novel array of new airspace users including Urban Air Mobility (UAM) concepts, personal air mobility vehicles, reusable space launch vehicles, and Unmanned Aircraft Systems (UAS). Focusing on UAS, there is much effort being directed towards the development of safety regulations for this industry. National Aviation Authorities (NAA) have advocated the adoption of a risk-based approach to the development of regulations, whereby regulations are driven by the outcomes of a systematic process to assess and manage identified safety risks. Central to a risk-based approach is the Safety Risk Management Process (SRMP). A review of relevant aviation safety policy, guidance and regulatory material found that aviation safety literature does not adequately address the uncertainty inherent to any SRMP. For example, when measuring risk, only the likelihood and severity are taken into consideration, with uncertainty generally not being mentioned. Where uncertainty is recognised, it is taken into consideration through the use of conservative worst-case assumptions. This can result in the imposition of overly stringent restrictions or worse, regulations that do not adequately mitigate safety risks. Subsequently, providing a more comprehensive treatment of uncertainty in the aviation SRMP is essential to the uptake of a risk-based approach to rule-making. Further, it follows that if assessments of performance can be uncertain, then these uncertainties also need to be accounted for in other NAA regulatory processes such as the regulatory compliance assessment and compliance finding processes. It was found that the current aviation compliance process does not provide an objective means for accounting for uncertainty. As a consequence, compliance assessments can be subjective and inconsistent, with regulators lacking the tools and processes to be able to make objective compliance findings on the basis of compliance risk. A means to enable NAA to account for uncertainty in regulatory compliance processes is needed. The overall aim of this thesis is to improve regulatory outcomes under the new paradigm of risk-based regulation, through providing a conceptual framework for the rational, transparent and systematic treatment of uncertainty in the risk assessment and regulatory decision-making processes. The thesis proposes the application of Bayesian methods and normative decision theory to the aviation safety regulatory process. System Safety Regulations (SSR), commonly referred to as "Part 1309" regulations, for UAS are used as a case study. It is posited that the general theoretical approach proposed in this thesis can improve the objectivity, consistency, and transparency of current aviation regulatory processes. The generalised approaches presented in this thesis enable the adoption of risk-based rulemaking for new aviation sectors and provides the theoretical basis for risk-based compliance; a paradigm shift in how aviation safety regulators approach risk-based regulation

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Rigorously assessing software reliability and safety

This paper summarises the state of the art in the assessment of software reliability and safety ("dependability"), and describes some promising developments. A sound demonstration of very high dependability is still impossible before operation of the software; but research is finding ways to make rigorous assessment increasingly feasible. While refined mathematical techniques cannot take the place of factual knowledge, they can allow the decision-maker to draw more accurate conclusions from the knowledge that is available

The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case