Event-specific analysis

Layer of protection analysis (LOPA) is a simplified form of event tree analysis. Instead of analyzing all accident scenarios, LOPA selects a few specific scenarios as representative, or boundary, cases. LOPA uses order-of-magnitLide estimates, rather than specific data, for the frequency of initiating events and for the probability the various layers of protection will fail on demand. In many cases, the simplified results of a LOPA provide sufficient input for deciding whether additional protection is necessary to reduce the likelihood of a given accident type. LOPAs typically require only a small fraction of the effort required for detailed event tree or fault tree analysis. 

Event Tree An event tree analysis begins widi a specific initiating event and works forward to evaluate potential accident outcomes. 

Event tree analysis is a teclmique for evaluating potential accident outcomes resulting from a specific initiating event. Tlie results of the event tree analysis are clironological sets of failures or errors that may define an accident. 

There are also special considerations as to how to statistically evaluate specific aspects of these studies. Specifically, analysis of time to event becomes very important (Anderson et al., 2000). 

CONSTRUCTING THE FAULT TREE. Fault tree construction begins at the top event and proceeds, level by level, until all fault events have been traced to their basic contributing events or basic events. The analysis starts with a review of system requirements, function, design, environment, and other factors to determine the conditions, events, and failures that could contribute to an occurrence of the undesired top event. The top event is then defined in terms of sub-top events, i.e., events that describe the specific "whens and wheres" of the hazard in the top event. Next, the analysts examine the sub-top events and determine the immediate, necessary, and sufficient causes that result in each of these events. Normally, these are not basic causes, but are intermediate faults that require further development. For each intermediate fault, the causes are determined and shown on the fault tree with the appropriate logic gate. The analysts follow this process until all intermediate faults have... 

An Event Tree Analysis (ETA), which explores aU possible outcomes of an undesired event (i.e. the specific crew error of concern), because it is only when there is a derived architecture that the exact role of humans in the system becomes clear. By using ETA the potential role of critical Human Error is evident, as are the combinations of Failures and errors necessary to create Hazardous or Catastrophic systems states. 

The HEP evaluation is initiated when the human related failure event is placed into the probabihstic model of the system. Then some attributes (factors) of such event are determined according to the procedure of given HRA method / technique. As the result the particular value of HEP is calculated. In the HRA and PSA (prohahihstic safety analysis) screening process only most important and more probable human failure events are taken into account for further context specific analysis. 

In this paper we explore quantitative risk assessment using a bow-tie model to analyze intangible risk -combining fault tree analysis and event tree analysis in order to estabUsh the cause/effect relations describing a specific imdesired event, see e.g. (Vatn et al. 1996). 

This task is only carried out if the semi-quantitative analysis does not provide sufficient information. In the quantitative analysis we may use FTA, network models of the infrastructure and capacities, and event tree analysis (ETA). As discussed by Kroger (2008), methods like FTA may have some shortcomings for analysis of complex interdependencies. However, to further investigate specific nodes of interest revealed in the cascade diagram (in Figure 4 and Figure 5), should not he more comphcated than in most other situations when FTA is useful. An example of use of FTA for assessment of power system rehahihty, can he found in Volkanovski et al. (2009). 

In the past decade several projects contributed to the luminosity distance measurements and by now (i.e., as of 2009) the list includes over 200 events. Specifically with the help of the Hubble telescope 13 new Sn la were found with spectroscopically confirmed redshifts exceeding z = 1 and at present the full sample contains already 23 z > 1 objects (Riess et al. 2007). Such objects most strongly influence the value of the deceleration parameter. A combined analysis of all Sn la data yields a deceleration parameter value of —0.7 0.1 (Kowalski et al. 2008). Its negative value signals an accelerating expansion rate at distance scales comparable to the size of the Universe. 

The engineering methods and techniques used for demonstrating the satisfaction of equipment safety requirements (e.g Fault Tree Analysis, Event Tree Analysis, Zonal Hazard Analysis etc.) are relatively well understood by the wider safety engineering community compared with those for people and procedures and will therefore not be discussed further here. The remainder of this paper will discuss how the above approach to safety requirements specification and realisation can be developed in the case of human-based subsystems, using Human Factors methods and techniques. 

ISO/lEC DIS 27039 [5] is a draft of a procedure, which describes an holistic approach of selection, deployment and operation of IDS in an organisation. It provides all four stages of the human-automation interaction process model, including event detection, analysis, response, and data storage. However, it fails to address cyber attacks (security) and user specific design approaches while handling IDS alerts. 

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