Risk Assessment Of Complex Systems Research Articles

Reducing the risk of intentional domino effects in process plants: A risk‐based minimax strategy

AbstractCompared with safety assessment, security risk assessment in chemical and process plants is more challenging. On top of uncertain environmental and operational parameters and interdependent failures, which are common in the safety risk assessment of complex systems and infrastructures, there are other uncertain parameters such as the likelihood of attack scenarios and attackers' expected outcomes. As such, the application of probabilistic risk assessment (PRA) techniques, which have long been applied to safety risk assessment and management, to security risk management may result in nonoptimal or suboptimal decisions. In the present study, we will demonstrate how a combination of PRA and game theory may outperform PRA and lead to a more cost‐effective allocation of security measures. For this purpose, the outcome of a dynamic Bayesian network—as a PRA technique—is used as input to the minimax strategy—as a game theoretic strategy—for security risk management of a tank terminal under attacks with a homemade bomb. The proposed risk‐based minimax strategy alleviates the need for estimation of attack likelihoods or attacker payoffs, which would have otherwise been too challenging to estimate if the analyst solely depended on a PRA technique.

Guided simulation for dynamic probabilistic risk assessment of complex systems: Concept, method, and application

Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate risky scenarios by the traditional PRA methods. In this study, a new dynamic probabilistic risk assessment methodology is proposed that employs a new exploration strategy to generate risky scenarios. The proposed methodology consists of three main modules, including simulation, planner, and scheduler. In this methodology, the engineering knowledge of the system is explicitly used to guide the simulation module to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the planner module which is responsible for generating plans as a high-level map to guide the simulation. The scheduler module is responsible for guiding the simulation by controlling the timing and occurrence of the random events. In this paper, modules of the proposed methodology, and their interactions are explained in detail. The developed methodology is used to perform risk assessment of a Space Shuttle ascent phase, and results show the effectiveness of the proposed platform.

Supervised Dynamic Probabilistic Risk Assessment of Complex Systems, Part 1: General Overview

Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past decades to evaluate risks associated with complex systems. However, current approaches to construct and execute DPRA models are challenged by high execution time owing to numerous possible scenarios. This issue will affect the execution time of the model, which is in contrast with the aim of modeling. DPRA models must be sufficiently fast to assist decision-making processes in the required time.In this study, a new method is proposed to enhance the execution times of DPRA models. This method uses optimization algorithms to determine failure scenarios and sort scenarios based on their occurrence probabilities. The most efficient optimization algorithms, considering the nature of the DPRA models, are mixed-integer sequential quadratic programming, modified branch-and-bound algorithm, and modified particle swarm optimization, which are then compared and discussed.To validate the effectiveness of this method, a simple case study is presented. The results show the effectiveness of the method, which has high accuracy and reduces the execution time significantly (e.g. execution time of risk assessment of 16,464 possible behavior scenarios after an incident in a dynamic positioning system is one fifth of the conventional methods). A detailed supervised DPRA model of dynamic positioning systems and its application on three incidents that occurred in the Norwegian offshore sector in previous years is presented in a subsequent article (Part 2 of 1) (Parhizkar et. al.). Case study results confirm that the supervised DPRA method can be applied to other complex systems so that the dynamic probabilistic risk values can be evaluated quickly and accurately.

Supervised dynamic probabilistic risk assessment of complex systems, part 2: Application to risk-informed decision making, practice and results

One challenge that has received attention in maritime industry is assessing the risk level of dynamic positioning (DP) systems in emergency situations. Statistics from recent years have shown that the risk level of some DP operations is above the industry's risk criteria. Operators have a significant impact on incidents’ consequences by making responsive decisions. In emergencies, one is afforded little time to make a decision. Available risk models are not efficient enough to provide systems’ risk level in a short period of time.In this study, the application of a new supervised methodology to assist decision making in emergencies is proposed. This method significantly reduces the processing and execution time of a system's probabilistic risk assessment models. In this methodology, the most probable failure scenarios are generated using an optimization model. The objective of the optimization model in this study is to find scenarios with the highest occurrence probabilities. The constraints are a system's dynamic simulation and its risk model. The proposed method is applied to three incidents that occurred in the Norwegian offshore sector in previous years. The results show that the model can predict the most probable scenarios with an acceptable accuracy in a very short time.

USING ABSOLUTE PROBABILITY JUDGEMENT METHOD FOR HUMAN RELIABILITY ASSESSMENT - A CASE STUDY

Human reliability assessment is becoming increasingly important in risk assessment in all industrial systems. All methods for human reliability assessment are used to estimate human error probabilities, which is a measure of human reliability. Human error assessment is certainly a challenge fo r all the experts involved in risk assessment today. In Serbia, this issue has not received proper attention yet. Therefore, this paper presents the case study which confirmed that the usage of Absolute Probability Judgement for proper human reliability assessment. This approach was used in a case study of the Electric Power Company in Serbia (hereinafter EPCS) for the purpose of the analysis accident of a repair intervention on a 10/0.4 kV steel lattice tower “Nova Kolonija” (jurisdiction of EPCS, Veliki Trnovac, ED “Jugoistok”, Nis, Serbia). The case study performed at the EPCS has confirmed that the conventional APJ approach is not only highly applicable for quantification of human errors, but also comprehensive and simple to use in risk assessment of complex systems. Key words: Absolute Probability Judgement, Human Reliability Assessment, Accident, Case Study

Dynamic risk assessment of complex systems using FCM

Analysing risk of today’s complex systems is challenging due to the complex and dynamic nature of systems. The current risk analysis tools are not able to take the complex interactions among risks into account and therefore they can’t predict the behaviour of risks accurately. In an attempt to overcome this shortcoming, this paper proposes an integrated generalised decision support tool using fuzzy cognitive maps for dynamic risk assessment of complex systems. The proposed approach has the ability to prioritise risk factors and more importantly predict and analysis the influences of each individual risk factor/risk set on the other risks or on the outcomes of complex and critical systems by taking into account probability of occurrence and consequences of risks and also considering the complex dependencies between risk factors. These features could provide practitioners with realistic results in critical industries and able them to manage risks more efficiently.

Success criteria analysis in support of probabilistic risk assessment for nuclear power plants: application on SGTR accident

Success criteria analysis (SCA) bridges the gap between deterministic and probabilistic approaches for risk assessment of complex systems. To develop a risk model, SCA evaluates systems behaviour in response to postulated accidents using deterministic approach to provide required information for the probabilistic model. A systematic framework is proposed in this article for extracting the front line systems success criteria. In this regard, available approaches are critically reviewed and technical challenges are discussed. Application of the proposed methodology is demonstrated on a typical Westinghouse-type nuclear power plant. Steam generator tube rupture is selected as the postulated accident. The methodology is comprehensive and general; therefore, it can be implemented on the other types of plants and complex systems.

A Heuristic Methodology for Efficient Reduction of Large Multistate Event Trees

This work proposes a new methodology for the management of event tree information used in the quantitative risk assessment of complex systems. The size of event trees increases exponentially with the number of system components and the number of states that each component can be found in. Their reduction to a manageable set of events can facilitate risk quantification and safety optimization tasks. The proposed method launches a deductive exploitation of the event space, to generate reduced event trees for large multistate systems. The approach consists in the simultaneous treatment of large subsets of the tree, rather than focusing on the given single components of the system and getting trapped into guesses on their structural arrangement.

Special Section on Reliability and Risk Assessment of Complex Systems

The 15 papers in this special section present new tools, techniques, applications, approaches, and surveys related to various aspects of reliability, and risk analysis and management.

CO2 Storage: Managing the Risk Associated With Well Leakage Over Long Time Scales

Summary One of the major challenges associated with the geological storage of carbon dioxide (CO2) is the performance of the confining system over long time scales. In particular, the occurrence of CO2 leakage through existing wells could not only defeat the purpose of storage, but also badly affect human health or the environment. Cement degradation and casing corrosion in injection, production, or abandoned wells can create preferential channels over time, allowing the migration of CO2 from the reservoir to shallower formations (e.g. aquifers), and/or to the surface. In this paper, a risk-based approach is proposed for well-integrity and confinement-performance management. The approach, based on Performance and Risk Management methodology (P&R™), serves as a decision-support tool. The major steps in this methodology are identifying the system and sources of degradation through characterization and system analysis; quantifying their criticality through modeling, in terms of probability and severity; and establishing a risk-mitigation plan. This methodology is based on experience in material aging and risk assessment of complex systems, such as nuclear structures where probabilistic simulations are performed. It accounts for all stakes involved in well-integrity management and enables the full integration of uncertainties as part of risk estimation. The methodology presented here greatly improves common approaches based on "features, events, and processes" because it quantifies risk levels. It provides useful and reliable tools to support decisions for well-integrity-management strategies or emergency plans. To that purpose, mitigation actions such as characterization/inspection, remediation (workover), design improvement, or monitoring are valued on the basis of a cost/benefit ratio. Moreover, updating the risk assessment with incoming data allows for an evolving vision of risk levels to optimize interventions over time. This approach has been applied successfully, leading to recommendations for safer and more-efficient design, maintenance, and monitoring strategies.

Call for papers on reliability and risk assessment of complex systems

Call for papers on reliability and risk assessment of complex systems

Integrating cyber attacks within fault trees

In this paper, a new method for quantitative security risk assessment of complex systems is presented, combining fault-tree analysis, traditionally used in reliability analysis, with the recently introduced Attack-tree analysis, proposed for the study of malicious attack patterns. The combined use of fault trees and attack trees helps the analyst to effectively face the security challenges posed by the introduction of modern ICT technologies in the control systems of critical infrastructures. The proposed approach allows considering the interaction of malicious deliberate acts with random failures. Formal definitions of fault tree and attack tree are provided and a mathematical model for the calculation of system fault probabilities is presented.

Analysing the risks of individual and collective intentionality

The risk assessment of complex systems often seems to neglect the way in which intentions, collective and individual, are central to our explanations of how risk arises in such systems. Contradictions among the intentions of different actors, for example, are typically an important part of our understanding of how organizations break down. Moreover, risk assessment practice pays little attention to the reflexive problem of how intentions for the risk assessment itself can themselves become problematic. This study was an attempt to develop a framework to support reasoning about intentionality, both individual and collective, during risk assessment. The framework broadly follows a process of 1) identifying the main social objects in a system, 2) asking what are the collective intentions for these objects in terms of the functions that are conferred on them, 3) asking what obligations and powers these create, and 4) asking what risks of organizational dysfunction can then arise. The approach was applied in a case study of aviation ramp operations. Its main value is as a formative rather than a summative kind of analysis.

System Hazard Analysis of the Selespeed Gearbox

This paper presents a method for the risk assessment of complex systems, with particular attention to the automotive field. It relies on a preliminary top-down analysis, similar to the one adopted in the development of Fault Trees, aimed at providing a graphical, high level, representation of the system functional dependencies and of the failure propagation paths. The resulting graph is pruned to consider any system failure, or top event, in any operating mode of interest and to isolate the components failures, or basic events, causing it. Finally, this information is collected in a Failure Mode and Effect Analysis (FMEA) table, which represents the final result of the procedure. The approach proposed has been used here to complete the hazard analysis of an automated manual gearbox.