Frequency analysis failure rate data

In some instances, plant-specific information relating to frequencies of subevents (e.g., a release from a relief device) can be compared against results derived from the quantitative fault tree analysis, starting with basic component failure rate data. 

Probability analysis Way to evaluate the likelihood of an event occurring. By using failure rate data for equipment, piping, instruments, and fault tree techniques, the frequency (number of events per unit time) can be quantitatively estimated. 

Data should be collected by the plant operators throughout the lifetime of the plant to check or update the analysis. These should include statistical data on initiating event frequencies, component failure rates and plant unavailability during periods of testing, maintenance or repair. The analysis should be assessed in the light of the new data. 

Fault Tree Analysis. Fault tree analysis, the most complex of the commonly used methods, is employed to determine the possible causes of a preselected undesired event. Through the use of logic diagrams and failure rate data, the team can make a quantitative evaluation of the frequency of the undesired event. 

Figure 3.2 shows a sample FMEA worksheet, and Figure 3.3 illustrates a sample CA worksheet per MIL-STD-1629A. Criticality is defined as a relative measure of the consequences of a failure mode and its frequency of occurrences. Early in the system design, in order to perform a useful Criticality Analysis (CA), parts configuration data and component failure rate data must be collected. 

The failure rates and times-to-restore developed used a variety of data sources and data construction methodologies and are presented in Section 2. The principal methodology used is a kind of failure mode analysis for each component several principle modes of failure are analyed by characteristics including frequency of occurence, repair time, start-up time, and shut-down time. From these an average failure rate is developed and expressed as failures per million hours and mean time between failure(MTBF). 

The hazard frequency assessment includes two part temporal frequency and spatial frequency. Temporal frequency is a hkehhood characteristic of rockfall occurrence per unit of time period i.e. one year. It indicates the rock instabiUty or failure in the source area. Temporal frequency can be inferred from historical catalogue or using relative rock failure rating system (Jaboyedoff et al., 2005). Distribution laws have been proposed for rockfaUs based on statistical analysis of historical data sets to derive their recurrent probability (Dussauge et al., 2002,2003). 

Within the overall aim it is the task of quantitative safety analysis to ascertain the frequency or occurrence probability of undesired events leading to incidents. Safety analysis will, in the case of problematic results of qualitative analysis, necessarily inspire the question of whether it should be continued in quantitative form. The question arises in particular when new technical equipment and processes are used. Quantitative safety analysis starts with knowledge of the logic structure of the system to be examined, as has already been ascertained in the course of qualitative analysis. A condition for execution is the presence of sufficient data—information about the behavior of the individual system components and parts. The information must be arranged in such a way that reliability characteristics (failure probabilities, failure rates) and maintenance characteristics (rates of repairs) can be derived. It is only when it is certain that sufficient data are available that quantitative analysis is possible. 

Fault trees, failure modes and effects analysis (FMEA), failure modes effects and criticality analysis (FMECA) and event trees use logic, reliability data (component failure rates), and assessed system failure rates, combined with human error failure rates (using methodologies such as HEART or THERP) and other methodologies such as software reliability assessment, to develop estimates of system failure frequencies, and hence plant accident frequencies. 

The validity of PRA results is determined in part by the quality of the data that is used in the quantification. Collection and analysis of data is therefore an important part of a reactor PRA. Data needed in order to perform a core damage frequency analysis include component failure rates, test and maintenance unavailabilities, initiating event frequencies, and human error rates. When possible, it is generally best to use... 

The data are very comprehensive with direct applications to reliability, risk, and event analysis of nuclear power plants. Information has been assembled on failure frequency, modes, repairs, and maintenance. Rate Information is based on demands calculated. The time period covered varies from the early 1970 s to the present. Using real time access, the output format if the event can be varied by selection of 20 generic and detailed categories. 

Expert opinion is a source, frequently elicited by survey, that is used to obtain information where no or few data are available. For example, in our experience with a multicountry evaluation of health care resource utilization in atrial fibrillation, very few country-specific published data were available on this subject. Thus the decision-analytic model was supplemented with data from a physician expert panel survey to determine initial management approach (rate control vs. cardioversion) first-, second-, and third-line agents doses and durations of therapy type and frequency of studies that would be performed to initiate and monitor therapy type and frequency of adverse events, by body system and the resources used to manage them place of treatment and adverse consequences of lack of atrial fibrillation control and cost of these consequences, for example, stroke, congestive heart failure. This method may also be used in testing the robustness of the analysis [30]. 

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