Components failure rate modeling

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. 

Modelling of the failure type of class (2) requires one to determine the expected frequency of the shock events and the corresponding conditional probabilities of component failures caused by them. The binomial failure rate model (BFR) is the best known model of this class. For its application observed CCF events are used to calculate the parameter of the binomial distribution [u in Eq. (9.36)]. This then enables one to determine the probabilities of failure combinations (e.g. three-out-of four redundant components) including for combinations which have not been observed. 

Note The FTA is primarily a graphical method using logic gates and fault events to model the cause— effect relationship in causing an undesired event This gr cal method can be translated into a mathematical model to compute failure probabiUties mid system importmice measures [Eiicson, 2005, Chapter 11]. This quantitative iproach provides more useful results, but requires mo e time (e.g. gathering of component failure rate data) mid expmenced personnel. 

An analytic RAM model has been developed for the availability assessments and decision situations described above. The model adapts both single-state and multi-state consequences of component/system failures. The FPSO system is modeUed by rehabhity block diagrams where each block represents a single component or a sub-system. Production availability figures of the sub-systems are estimated based on the system configuration. Finally, the total system availability is estimated. The component availabihties are calculated based on the component-failure rates and... 

Additive models do not really consider the structure of the software but instead attempt to predict the time-dependent failure rate forthe compoimd software from the components failure data. In e.g. (Xie and Wohlin 1995) it is assumed that any component failure causes system failure (i.e. aU components are assmned to be in series with each other), and the system failure rate Xs(t) is obtained from the components failure rates through... 

When modeling the failure to respond event the one out of two arrangement represents redundancy and the two subsystems are said to be parallel in that they both need to fail to cause the event. Furthermore the component failure rates used will be those which lead to ignoring a genuine signal. On the other hand, if we choose to model the spurious shutdown event the position is reversed and the subsystems are seen to be series in that either failure is sufficient to cause the event. Furthermore the component failure rates will be for the modes which lead to a spurious signal. 

This involves assessing the design, by means of reliability analysis techniques, to determine whether the targets can be met. Techniques include fault tree and logic block diagram and FMEA analysis, redundancy modeling, assessments of common cause failure, human error modeling, and the choice of appropriate component failure rate data. Reliability assessment may also be used to evaluate potential financial loss. The process is described in Work Instruc-tion/001 (Random hardware failures). 

Spreadsheet (or FARADIP output) showing, for each failure mode of the equipment, the component failure rates, and modes (for each block identified in the reliability/ fault model) the data source used (with any justifications if necessary) Section of this... 

The second PoF-based failure behavior modeling method is the Failure-Rate-oriented (FR-oriented) method. The method uses failure rates as a measure of system failure behaviors. A typical example of the method is the RAMP method developed by IBM cooperation (Srinivasan et al. 2003). Under the assumptions of constant failure rate and failure competition, the method firstly calculates the Mean Time To Failure (MTTF) of each component from corresponding PoF models. Then, failure rates of the system are established by summing up all components failure rates. 

This paper describes a method for automated safety analysis of a technical system that is represented as a multi-domain object-oriented model. The proposed method automatically detects the minimal path sets of the modelled system. Then, the probability of system operation or failure is computed from the minimal path sets using component failure rates. 

Insertion of component failure rates Failure rates X are stored in each component model that is enhanced with failures. Constant failure rates (exponentially distributed lifetimes) are assumed per default. Since the stress level of a component is known in the simulation, its failure rate can be adapted accordingly. Failure rates are used to compute probability of system operation Rsyff) or failure from the detected minimal path sets. 

NSWC-94/L07 - Handbook of Reliability Prediction Procedures for Mechanical Equipment. This handbook presents a unique approach for prediction of mechanical component reliability by presenting failure rate models for fundamental classes of mechanical components. 

The handbook includes a series of empirical failure rate models developed using historical piece part failure data for a wide array of component types. There are models for virtually all electrical/ electronic parts and a number of electromechanical parts as well. All models predict reliability in terms of failures per million operating hours and assume an exponential distribution (constant failure rate), which allows the addition of failure rates to determine higher assembly reliability. The handbook contains two prediction approaches, the parts stress technique and the parts count technique, and covers 14 separate operational environments, such as ground fixed, airborne inhabited, etc. 

Examples of the specific mechanical devices addressed by the document include belts, springs, bearings, seals, brakes, slider-crank mechanisms and clutches. Failure rate models include factors that are known to impact the reliability of the components. 

The objective is to estimate, numerically, the probability that a system composed of many components will fail. The obvious question is, "Why don t you just estimate the failure rate of the system from operating experience " There are three reasons IJ the system may not exist, so new data are not available, 2) the injuries and fatalities from the developmental learning experience are unacceptable - the risk must be known ahead of time, and 3) by designing redundancy, the probability of the system failing can be made acceptably remote in which case system failure data caimot be collected directly. The only practical way uses part failure statistics in a system model to estimate the system s reliability. 

We previously encountered failure modes and effects (FMEA) and failure modes effects and criticality analysis (FMECA) as qualitative methods for accident analysis. These tabular methods for reliability analysis may be made quantitative by associating failure rates with the parts in a systems model to estimate the system reliability. FMEA/FMECA may be applied in design or operational phases (ANSI/IEEE Std 352-1975, MIL-STD-1543 and MIL-STD-1629A). Typical headings in the F.Mld. A identify the system and component under analysis, failure modes, the ef fect i>f failure, an estimale of how critical apart is, the estimated probability of the failure, mitigaturs and IHissihiy die support systems. The style and contents of a FMEA are flexible and depend upon the. ilitcLiives of the analyst. 

USC may be modeled as a power-series expansion of non-CCF component failure nates. No a priori physical information is introduced, so the methods are ultimately dependent on the accuracy of data to support such an expansion. A fundamental problem with this method is that if the system failure rate were known such as is required for the fitting process then it would not be neces.sary to construct a model. In practice information on common cause coupling in systems cannot be determined directly. NUREG/CR-2300 calls this "Type 3" CCF. 

Fleming et al. (1985) define this as similar to the model of Marshall and Olkin (1967) except that BPM is only for time-dependent failure rates. Equations 3.5.8-la-d are for four parameters, but the method may be generalized to n components. These parameters may be related to the MGL parameters as shown in equations 3.5.8-2a-d. 

The Rome Air Development Command (RADC - Rome NY) provides the MIL HDBK 217 series of detailed electronics information. Early reports in this series provided failure rates for electronic components. The development of integrated circuits resulted in the approach of providing parameters for mathematical models of transistors and integrated circuits. RADC also publishes Nonelectronic Parts Reliability Data covering the failure rates of components ranging from batteries to valves. 

The accident sequence frequencies are quantified by linking the system fault tree models together as indicated by the event trees for the accident sequence and quantified with plant-specific data to estimate initiator frequencies and component/human failure rates. The SETS code solves the fault trees for their minimal cutsets the TEMAC code quantitatively evaluates ihe cm sols and provides best estimates of component/event probabilities and frequencies. 

The failure data relating to electronic and electric components are available in the form of handbooks. Failure rates are derived with the aid of calculation models based on statistical relations for which the incorporation of a (large) number of parameters is required. The following minimum of information is needed type of component, manufacturer and environmental factors. 

This report presents a set of failure rate and time-to-restore data for typical components of a coal gasification combined cycle power generation unit. The data was used to examine the reliability and availability of a generic power generation unit using risk analysis models. 

A full-scale model of the air intake manifold, where the complete sensor (with signal processing ICs, housing, cable fittings, etc.) can be tested under real-life conditions, was available. However, failure rates of components in automotive applications under normal conditions are on the order of a few ppm, which corre-... 

Each component or part group and its associated subgroup has a base failure rate plus numerous tt factor tables, unique to that component or part, that list factors that are used in the model to adjust the base failure rate. 

Once a fault tree has been developed, failure rate data for individual components in the system can be entered into the tree so that an estimate of the likelihood of the undesired event (the Top Event ) can be made. Frequently the quality of the failure rate data is poor nevertheless, through use of the Pareto Principle or 80/20 rule discussed above, a quantified analysis still provides useful insights because it identifies which items in the system contribute the most to system failure. Moreover, once the model has been developed, and preliminary estimates as to failure rates have... 

Final element assemblies are modeled like any combination of components using the system reliability engineering techniques. Failure mode classification is generally straightforward. The key variables to include in the evaluation of failure rates are ... 

Since state 0 is the success state, reliability is equal to So(t) and is given by Equation D-11. Unreliability is equal to Sj(t) and is given by Equation D-12. This result is identical to the result obtained when a component has an exponential probability of failure. Thus, the Markov model solution verifies the clear relationship between the constant failure rate and the exponential probability of failure over an interval of time. 

In order to compare architectures accurately, the failure rate for the diagnostic channel must be estimated as it is likely that extra components will be required. These failure rates must be added to those for the single board PEC to obtain totals. Table F-8 shows the model parameters including the new failure rates. 

It must be emphasized that a component whose lifetime is exponentially distributed cannot be improved by maintenance. For an improvement would imply a reduction of its failure rate. In the present model it is ensured that the unavailability is equal to zero after every functional test. This is achieved by determining in the first place whether it is still capable of functioning or has failed. In the latter case the component is either repaired or replaced. If it is still capable of functioning it is as good as new because components with a constant failure rate do not age by definition. If it has to be repaired, as good as new is a hypothesis usually corroborated in plants with a good safety culture. 

The Beta Factor Model is a one parameter model in which the total failure rate of a component is split into an independent part and one due to common cause, i.e. 

Depending on whether we deal with a risk-based or a detailed risk study the scope of the failure mechanisms represented by the failure rates must differ. For risk-based analyses the failure rates should represent besides spontaneous failure failures caused by impermissible loads on structural materials following malfunctions or operator errors. Since the latter are explicitly modelled in a detailed risk analysis the failure rates for passive components used there should only represent the spontaneous part. The scope of the failure mechanisms covered is usually not described in suflhcient detail and can practically not be determined a posteriori [5]. 

In the procedure of probabilistic modeling of E/E/PE systems the diagnostic coverage (DC) parameter allow to obtain for each component of given category the failure rate (danger undetected, danger detected, safe undetected and safe detected). It is obtained from some tables in lEC 61508 and expert opinions. 

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