Event tree analysis branch

The relevant data for the quantification of the event tree analysis are the conditional probabilities for the branch points. There is considerable uncertainty in the phenomena that would occur and consequently the probabilities used are often based on expert judgement. 

Event Tree Analysis (ETA) is used where appropriate to model all the possible outcomes of a hazard taking account of the mitigations (usually external to the system element in question) that could be used to break an accident sequence ould a hazard occur. Working from left to right, each branch of the Event Tree represents a mitigation to which probabilities can be applied in order to express the relative likelihood of success (S) or failure (F) of the mitigation. 

Event tree analysis (ETA) is a logical representation of the various events that maybe triggered by an initiating event (e.g., a component failure). It uses branches to show the various possibilities that may arise at each step and it is often used to relate a failure event to various consequence models. It may also be used to quantify system-failure probabilities, where several contributory causes can only arise sequentially in time. 

Fault Tree Analysis. Fault trees represent a deductive approach to determining the causes contributing to a designated failure. The approach begins with the definition of a top or undesired event, and branches backward through intermediate events until the top event is defined in terms of basic events. A basic event is an event for which further development would not be useful for the purpose at hand. For example, for a quantitative fault tree, if a frequency or probabiUty for a failure can be deterrnined without further development of the failure logic, then there is no point to further development, and the event is regarded as basic. 

Frequency Phase 3 Use Branch Point Estimates to Develop a Ere-quency Estimate for the Accident Scenarios. The analysis team may choose to assign frequency values for initiating events and probability values for the branch points of the event trees without drawing fault tree models. These estimates are based on discussions with operating personnel, review of industrial equipment failure databases, and review of human reliability studies. This allows the team to provide initial estimates of scenario frequency and avoids the effort of the detailed analysis (Frequency Phase 4). In many cases, characterizing a few dominant accident scenarios in a layer of protection analysis will provide adequate frequency information. 

The branching probability at a node is determined by either fault tree analysis of the event system or by data from operating experience. 

A probabilistic statement of the likelihood of human-error events presents each error in the task analysis as the right limb in a binary branch of the HRA event tree. These binary branches form the chronological limbs of the HRA event tree, with the first potential error siai ting from the highest point on the tree. (Figure 4.5-4). Any given [ask appears as a two-limb branch the left limb represents the probability of success the right limb represents the probability of failure. 

The development of the HRA event tree is one of the most critical parts of the quantification of human error probabilities. If the task analysis lists the possible human error events in the order of ihcir potential occurrence, the transfer of this information to the HRA event tree is fadlitutcd. Each potential eiTor and success is represented as a binary branch on the HRA event tiec. with subsequent errors and successes following directly from the immediately preceding ones. Cure should be taken not to omit the errors that are not included in the task analysis table but might affect the probabilities listed in the table. For example, administrative control errors that affect a task being performed may not appear in the task analysis table but must be included in the HRA event tree. 

The initiators are separated into two classes those for which the event iree/fauJt tree analysis is appropriate and those for which it is not. The former are called internal initiators and the latter, external initiators (externalities). If dependencies are accounted for by modifying the branching probabilities of the event tree, both internal and external initiators can be accounted for in the same event tree. 

The systems list, across the top of the event tree, specifies the systems that must be analyzed to obtain the branching probabilities of the event tree. For complex reliable systems, fault tree or equivalent analysis may be needed to obtain system probability from component probabilities. For less reliable systems, the branching probability may be obtained from plant records with cautions regarding system interactions. 

The first step of the analysis is to identify the human actions and equipment failures that can lead to the failure of interest. An HRA event tree (Figure 5.14) is then constructed to depict the potential human errors (represented by capital English letters) and the potential equipment failures (represented by capital Greek letters). The series of events that will lead to the failure of interest is identified by an Fi at the end of the last branch of the event tree. All other outcomes are considered successes even though the propane release is not isolated in outcomes S2 and S3, so the process must be shut down. 

The use of event trees is sometimes limiting for liazard analysis because it may lack die capability of quantifying die potendal of die event occurring. Tlie analysis may also be incomplete if all inidal occurrences are not identified. Its use is beneficial in examining, rather dian evaluating, die possibilities and consequences of a failure. For this reason, a fault tree analysis (FTA) should supplement diis, to establish die probabilities of die event tree branches. Tliis topic was introduced in a subsection of Cliapter 16. 

Figure 21.7.2 demonstrates die preliminary steps for a fault tree analysis (FTA) in addition, die TOP event, bounds, configurations, and unallowed events are specified, and die level of resolution is shown. Once all die limits have been detennined, die fault tree is constructed (Figure 21.7.3). Note tliat every branch of die fault tree ends in a basic fault or cause leading to die TOP event.

For acute releases, the fault tree analysis is a convenient tool for organizing the quantitative data needed for model selection and implementation. The fault tree represents a heirarchy of events that precede the release of concern. This heirarchy grows like the branches of a tree as we track back through one cause built upon another (hence the name, "fault tree"). Each level of the tree identifies each antecedent event, and the branches are characterized by probabilities attached to each causal link in the sequence. The model appiications are needed to describe the environmental consequences of each type of impulsive release of pollutants. Thus, combining the probability of each event with its quantitative consequences supplied by the model, one is led to the expected value of ambient concentrations in the environment. This distribution, in turn, can be used to generate a profile of exposure and risk. 

However, this tree was unwieldy, and the SST had reached consensus early in its investigations that none of the visualized challenges would have caused the polymerization if there had been sufficient inhibitor in the material to start with. In order to expedite tree analysis, the subcommittee decided to concentrate only on the inhibitor system branch of the tree. This also allowed us to concentrate on the effects of changes in the system without the unnecessary complications of second-order effects that were not well quantified and which could not be controlled. (The weather in Houston is going to be hot in July, and there isn t much we can do about it.) The top event in all subsequent versions of the trees was Insufficient inhibitor to meet stability specifications. ... 

Finally the most rigorous method is the Fault Tree Analysis. In this method, a specific undesired process event such as an explosion is postulated and placed at the top of a tree, from which branches representing all possible precursor events or causes are extended. When basic causes are reached, failure rates are estimated or obtained. While much has been written about Fault Tree Analysis, this method has not been used at our research site because less rigorous methods are more suited to our rapidly changing research environment. It has been extensively used at our production sites, however. 

Fault Tree Analysis - working backwards from a failure or potential failure, this logical procedure identifies all the possible causes, and hence, the origins of that failure. The fault tree consists of branches connected by AND and OR nodes - all the branches below an AND node need to coincide for the event above the node to occur, but only one of the branches below an OR node is required to condition the same. In these terms, a cause-effect map of failure is constructed. The advantage of such analysis is that it codifies a common understanding of the intrinsic logic of failure possibility. 

THERP is usually modeled using a probability tree. Each branch represents a task analysis showing the flow of task behaviors and other associations. A probability is assigned based on the event s occurrence or nonoccurrence. 

Human reliability analysis is an important component of risk analysis. Reviews of past accidents show that human error accounts for the vast majority of these events. The technique most widely used for estimating human error probabilities is called THERP (Swain and Guttman, 1983). The method uses event trees drawn in a different format to arrive at a human error probability. See Fig. 10.15 for an example. In these event trees, failure paths branch right and success paths branch left. 

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