Why Risk Models should be Parameterised

Risk Analysis, 2010

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed.

Computer Aided Chemical Engineering, 2007

This paper presents the use of functional modeling for risk analysis. Many methods exist to perform hazard analysis but they are not based on (explicit) models of the plant. The use of a model can help in having a semiautomatic and consistent way to perform the analysis. Besides the model can be, with little effort, reused in similar processes. A functional modeling methodolody is applied to a real process to prove the suitability of these techniques to perform risk analysis.

Research, Practices, and Innovations in Global Risk and Contingency Management, 2018

World is vicinity full of opportunities given the amount of economic and non-economic transactions taking place every moment. With ubiquitous opportunities all around, businesses can assume inherent risk everywhere in one or the other way. In this chapter, the authors have deliberated the general business scenario to prove the given inferences. The readers will come across why the risk management is gaining so much gravity and across risk strategy of top business players. The chapter will bring into light the various risk factors in business and study the various risk assessment models present to fortify the negativity of these risk factors. Simultaneously, the authors will draw empirical evidence on the effectiveness, qualitative and quantitative risk models have on risk factors in public and private business organisations.

2005

This paper has by objective to presents an approach of analysis of exceptional contingency in technical systems looking for imprecision and scarceness of historical data treatment. The basics goals of a contingency analysis are to identify, model and quantify in a proper manner the risks associated with a system operation and maintence. Unlike traditional methods of Probabilistic Risks Analysis, the approach used in this article tries to bypass some specific problems found in these kinds of methodologies. The risk perception in these methods and its probabilistic modeling requires historical data and a precise formulations of risks and ways which take to a failure. In many situations, data are nor registered or are imprecisely registered. This paper proposal consists in presenting a risks quantification procedure based on bayesian inference, on which can be used a specialist knowledge for treating the lack of data. This subjective information is them actualized by a bayesian update. The event quantification result generates an inference tool which represents the probabilities distribution of identified exceptional contingence occurrence. This contingences analysis provides as results, probabilities that orient the establishment of emergency actions plans for contingences.

Safety in the use of available resources requires serious consideration of test and evaluation strategies that provide an accurate assessment of risks. Risk management, as a continous process involves different aspects of possible hazards. In this paper, we present a general modelling of risk management through modelling of identification, analysis and risk evaluation. A grade for quantitative classification of risk is defined, which enables a clear distinction between the case when the risk is low and its assessment is not required and the case of extremely high risk which is practically impossible to reduce. The proposed model is applied to the real world case study as the analysis of risk for the environment of a driver. Based on the collected data, regarding the difficulty of work, emotional and mental load, micro climate parameters and amount of light, hazards are identified, established and risk levels of potential undesired events are assessed.

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2006

Modeling and analyzing risk is one of the most critical activity in system engineering and approaches like Fault Tree Analysis, Event Tree Analysis, Failure Modes and Criticality Analysis have been proposed in literature. All these approaches focus on the system-to-be without considering the impact of the associated risks to the organization where the system will operate. On the other hand, the tendency is more and more to consider software development as a part of organizational development. In this paper, we propose a framework to model and reason about risk at organizational level, namely considering the systemto-be along the organizational setting. The framework extends Tropos, a methodology that has been proved effective in modeling strategic interests of the stakeholders at organizational level. We introduce a number of different means that help the analyst to identify and enumerate relevant treatments for risk mitigation. Experimental results are finally presented and discussed.

Risk Analysis, 1998

Importance measures have been usefd in the process of extracting insights from risk analyses. Importance measures have also been suggested for use in component classification. However, there are difficulties of interpretation associated with component classification based on importance measures. This paper briefly reviews an alternative method, "Top Event Prevention Analysis," and, based on a simple example, compares its key characteristics to those of conventional applications of importance measures. The methods are compared with respect to the task of formulating a safety case for a complex and potentially hazardous facility, in which component classification plays an important role. A key subtask is identification of a collection of design elements that is necessary and sufficient to achieve the desired level of protection of the public, the workers, and the environment. At the design stage, identifying this set helps to determine what elements to include in the final design. Separately, a similar selection process could be used in order to justify limiting the scope of regulatory oversight to a subset of design elements, on which a safety case is to be based. This step could be taken during initial review of a design, or later as part of an effort to justify relief from regulatory requirements that are burdensome but provide little actual risk reduction.

Applied Mathematical Sciences

Recently the classical actuarial risk model for the evaluation of the ruin probability has been generalized to include dependency relations between the claim occurrences times and the claim amounts. For instance it has been incorporated into the stochastic model that the distributions of the times between consecutive claim occurrences times may depend on the last previous claim amount [1] or that claim sizes depend on the past of the point process of instants when claims are presented [2,3]. Results on ruin probabilities and related quantities have been published in several papers under such assumptions. This evolution implies that in a concrete application we have to choose between different versions of the actuarial risk model. This choice should be performed in a reasonable and objective way taking into account our empirical knowledge of the risk process. This leads us naturally to develop statistical tests able to distinguish certain classes of marked point processes. In order to assure the optimal use of the data it seems indicated to look for locally most powerful tests [4] and a clever choice of the period of observation of the risk process greatly facilitates the task and leads to simple procedures which are easy to implement. Keywords: Actuarial risk models, dependencies between claim occurrences times and claim amounts, (locally) most powerful statistical tests, embedded renewal processes, choice of the period of observation of the risk process

Journal of energy and power engineering, 2020

The paper discusses the framework for a risk-informed root cause analysis process. Such process enables scaling of the analysis performed based on the risk associated with the undesired event or condition, thereby creating tiers of analysis where the greater the risk, the more sophisticated the analysis. In a risk-informed root cause analysis process, a situation is normally not analyzed at a level less than what actually occurred. However, a situation may be investigated as though the consequence were greater than actually happened, especially if only slight differences in circumstances could result in a significantly higher consequence. While operational events or safety issues are normally expected to result only with negligible or marginal actual consequences, many of those would actually have certain potential to develop or propagate into catastrophic events. This potential can be expressed qualitatively or quantitatively. Risk-informing of root cause analysis relies on mapping the event or safety issue into a risk matrix which, traditionally, is a two-dimensional probability-consequence matrix. A new concept employed in the risk matrix for root cause analysis is that, while the probability reflects the observed or expected range of values (retaining, thus, its "traditional" meaning), the consequence reflects not only the observed or materialized impact (such as failure of equipment) but, also, its potential to propagate or develop into highly undesirable final state. The paper presents main elements of risk-informed root cause analysis process and discusses qualitative and quantitative aspects and approaches to determination of risk significance of operational events or safety issues.