Using Failure Rate Data

When using failure rate data for a CPQRA, the ideal situation is to have valid historical data from the identical equipment in the same application. In most cases, plant-specific data are unavailable or may carry a level of confidence that is too low to allow those data to be used without corroborating data. Risk analysts often overcome these problems by using generic failure rate data as surrogates for or supplements to plant-specific data. Because of the uncertainties inherent in risk analysis methodology, generic failure rate data are frequently adequate to identify the major risk contributors in a process or plant. 

This chapter has discussed some of the factors that may affect equipment reliability and necessitate data adjustment. At this time, little documented assistance is available to help develop these data adjustments. It may be necessary to get help from experts in some situations. Lastly, failure rates are often reported to several decimal places, a precision frequently unwarranted by the data. It is suggested that only the failure rate s first significant number and associated exponential power be used. 

Guide to the Collection and Representation of Electrical, Electronic, Sensing Component, and Mechanical Equipment Reliability Data for Nuclear Generating Stations. IEEE Std. 500-1984, Institute of Electrical and Electronic Engineers, New York, 1984. 

Borkowski, R. J., Pike, D. H., and Goldberg F. F. The In-Plant Reliability Data Base for Nuclear Power Plant Components Data Collection and Methodology Report. NUREG/ CR-2641, ORNL/TM-9216, January 1985. 

Offshore Reliability Data Handbook, OREDA-84. P.O. Box 370, N-1322, HOVIK, Norway, 1984. Distributed by Pennwell Publishing Company, Tulsa, OK. 

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To properly use failure rate data, the engineer or risk analyst must have an understanding of failure rates, their origin and limitations. This chapter discusses the types and source of failure rate data, the failure model used in computations, the confidence, tolerance and uncertainties in the development of failure rates and taxonomies which can store the data and influence their derivation. 

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. 

Current functional safety standards, lEC 61508 and ANSl/lSA-84.00.01-2004 (lEC 61511 Mod), (Ref. 1 and 2) state that probabilistic evaluation using failure rate data be done only for random failures. To reduce the chance of systematic failures, the standards include a series of "design rules" in the form of specific requirements. These requirements state that the safety instrumented system designer must check a wide range of things in order to detect and ehcninate systematic failures. 

If so, that could be a valid SIF description. If however the hazard would be prevented if either XV-01 OR XV-02 closes, then case two is a much better description. If all three final elements are required to operate then case three is the best description. The results of the SIL verification calculations for case 1, 2, and 3 using failure rate data for generic components are shown in Table 14-5. 

For more on how to use failure rate data to calculate MTBF, see Kritzinger (2006, paragraph 10.2.4). 

The numbers computed usiag this approach are only as good as the failure rate data for the specific equipment. Frequendy, failure rate data are difficult to acquire. For this case, the numbers computed only have relative value, that is, they are useful for determining which configuration shows iacreased reUabiUty. 

Once the fault tree is constructed, quantitative failure rate and probability data must be obtained for all basic causes. A number of equipment failure rate databases are available for general use. However, specific equipment failure rate data is generally lacking and. 

The event" list, across the top of the event tree, specifies events for which the probability of failure (or success) must be specified to obtain the branching probabilities of the event tree. Events that are the failure of a complex system may require fault tree or equivalent methods to calculate the branching probability using component probabilities. In some cases, the branching probability may be obtained directly from failure rate data suitably conditioned for applicability, environment and system interactions. 

ANSPIPE Calculates pipe break probability using the Thomas Model BETA Calculates and draws event trees using word processor and other input BNLDATA Failure rate data... 

This chapter introduces the need for process equipment failure rate data, defines the scope and organization of this book and the data it contains, and explains how to the use the book. 

Chapter 2—Origin, Use, and Limitations of Failure Rate Data Explains the meaning of generic and plant-specific data, the difference between time-related and demand-... 

When plant-specific data are required. Chapter 6 discusses how to collect and treat the data so that the resulting failure rates can be used in a CPQRA or be combined with the data in the CCPS Generic Failure Rate Data Base. Chapter 7 provides a form that can be used to transfer these data to CCPS s Generic Failure Rate Data Base. 

Both of the sources above contain tWo types of failure rate data used in CPQRAs time-related failure rates and demand-related failure rates. Time-related failure rates, presented as failures per 10 hours, are for equipment that is normally functioning, for example, a running pump, or a temperature transmitter. Data are collected to reflect the number of equipment failures per operating hour or per calendar hour. 

Tolerance uncertainty arises from the physical and the environmental differences among members of differing equipment samples when failure rate data are aggregated to produce a final generic data set. Increasing the number of sources used to obtain the final data set will most likely increase the tolerance uncertainty. 

A failure rate generated from collecting data on a system will be dependent upon all the circumstances under which the system operates. Consequently, the failure rate data should only be used for predictions on a system in which the circumstances are identical. Otherwise, the failure rate applicable to the second system will need to be adjusted. 

This chapter provides summaries of seleeted data resourees available to the CPQRA praetitioner. These resourees are summarized in a eonsistent format that allows them to be easily reviewed and eompared. Those resources whieh are available to CCPS and contain equipment failure rate data of suffieient quality are used for the data tables in Seetion 5.5. 

The component failure rate data used as input to the fault tree model came from four basic sources plant records from Peach Bottom (a plant of similar design to Limerick), actual nuclear plant operating experience data as reported in LERs (to produce demand failure rates evaluated for pumps, diesels, and valves), General Electric BWR operating experience data on a wide variety of components (e.g., safety relief SRV valves, level sensors containment pressure sensors), and WASH-1400 assessed median values. 

This chapter contains tables of generic equipment failure rate data for some of the CPI equipment types listed in Appendix A, the CCPS Taxonomy, or in Appendix B, the Equipment Index. Section 5.1 on data selection explains how data were selected from resources and lists which resources in Chapter 4 were used to provide data. 

Section 5.4 describes the use of the CCPS Generic Failure Rate Data Base. Lastly, Section 5.5 contains tables of data in the Generic Failure Rate Data Base, organized by the numbers used to structure the CCPS Taxonomy. 

SAIC provided much of the data used in this book from its proprietary files of previously analyzed and selected information. Since these data were primarily from the nuclear power industry, a literature search and industry survey described in Chapter 4 were conducted to locate other sources of data specific to the process equipment types in the CCPS Taxonomy. Candidate data resources identified through this effort were reviewed, and the appropriate ones were selected. Applicable failure rate data were extracted from them for the CCPS Generic Failure Rate Data Base. The resources that provided failure information are listed in Table 5.1 with data reference numbers used in the data tables to show where the data originated. 

Note SAIC has selected some data from resources 8.1 through 8.15 to construct its proprietary data files for use in performing PRAs. Relevant data from these files was used to construct the CCPS Generic Failure Rate Data Base. Accordingly, all usable data points contained in the resources used by SAIC may not be in the Data Tables in this book. 

Failure rate data selected for the CCPS Generic Failure Rate Data Base were handled using dBase III Data Management in conjunction with the Computerized Aggregation of Reliability Parameters (CARP) developed by SAIC. CARP, designed to be used by... 

Use of the CCPS Generic Failure Rate Data Base... 

As explained in Section 3.3, failure rate data for a piece of equipment or system can be located by the taxonomy number for the equipment. The number can be found by using the CCPS Taxonomy, Appendix A, or the alphabetized hardware list in the Equipment Index, Appendix B. Table 5.2 shows whether the CCPS data base contains failure rate data for that numbered data cell or for an appropriate higher-level cell. Alternatively, the user may look directly for the desired taxonomy cell in the data tables. 

A different set of forms, in extensive use for failure rate calculation, are used to illustrate the remaining sections of this chapter. Beginning with Figure 6.3, the forms present a worked pump example for the conversion of actual plant raw data to plant-specific failure rate data. 

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