Asymmetrical Common-Cause Failures Analysis Method Applied in Fusion Reactors

PurposeThis paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.Design/methodology/approachFirstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.FindingsIn this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.Originality/valueThe proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

Reliability analysis of hierarchical computer-based systems subject to common-cause failures

Common cause events are those specific groups of dependent events that might adversely affect the operation of a redundant system. Common cause failures (CCF) are considered a subset of dependent failures with a major difference that they cannot be explicitly modeled. A CCF is a single point of failure (SPOF) causing a unit and its "perceived" redundant unit to fail simultaneously. During the first few years of commercial nuclear power plant operations, many probability risk assessment (PRA) studies revealed that CCF have significantly contributed to core damages. Consequently, the U.S. Nuclear Regulation Commission (NRC) initiated various CCF analyses in the 1980s. Since then, CCF analyses have been synonymous with nuclear facilities, and a majority of CCF analysis efforts have rightfully been focused on the safety of nuclear plants. Given that many industrial and commercial mission-critical facilities and high-reliability organizations now require a high degree of availability, therefore, CCF cannot be ignored. The objectives of this paper are to present 1) the importance of considering CCF in reliability, availability, and maintainability (RAM) analysis for industrial and commercial mission-critical facilities and high-reliability organizations, 2) simple quantitative analysis methods of CCF for industrial and commercial mission-critical facilities, and 3) the need for including CCF as part of future failure data collection efforts.

Common cause failure analysis of PWR containment spray system by GO-FLOW methodology

A k-out-of- N:G redundant system with dependent failure rates and common-cause failures

A reliability analysis of a k-out-of-N:G redundant system with common-cause failures and critical human errors

Modeling and quantification of simultaneous failures of multiple components due to common causes is a frequent and often crucial task in safety system reliability and risk assessments. Several models of common cause failures (CCF) are introduced. Their mutual relationships, limitations, and advantages, as well as sources of parametric values, are described. The models are presented in terms of basic multiple‐failure rates and probabilities that are independent of system testing schemes and intervals, and have direct connection to the basic event probabilities needed in system logic models.The early CCF models such as the β factor, binomial failure rate, common load, and distributed failure probability models are all special cases of stochastic reliability analysis. Even if mathematically attractive, these models are inherently constrained by certain mutual couplings between the CCF rates or CCF probabilities.More general α factor and multiple Greek letter models are based on the ratios of multiple‐failure rates or probabilities. In applications, both models need some additional failure rate from outside of the model to yield CCF rates and basic event probabilities.Extensive national and international common cause failure data collection efforts are making it feasible to estimate plant‐specific multiple‐failure rates and probabilities by combining data from many similar systems with empirically oriented Bayesian methods without assuming common generic parametric values. This methodology is briefly described. It takes into account both statistical and assessment uncertainties due to limited observations, interpretations, and documentation of CCF events.

A reactor protection system (RPS) in a boiling water reactor (BWR) is a unique system since it represents high redundancy compared with other safety-related systems in the BWR. Owning to its high redundancy, NUREG-5500 vol.3 showed that unreliability of the General Electric BWR4-type RPS is dominated by common-cause failures (CCFs) in its highly redundant components. Since the staggered test scheme can reduce the period of latent CCF states of redundant standby components, the scheme is a good candidate to improve the reliability of the RPS. However, the scheme is impracticable for the RPS because of its high redundancy. In this paper, a new concept of a “multi-group staggered test scheme” for a highly redundant system is proposed. In the multi-group staggered test scheme, components are arranged into several groups, and all components in a group are tested simultaneously. This paper presents a preliminary example of the new scheme that is applied to control rod notch tests, in which all withdrawn control rods are moved at least one notch. It is shown that the new scheme can reduce the CCF risk in RPS.

The reliability is the probability that a device will perform its required function under stated conditions for a specified period of time. The Common Cause Failure (CCFs) is the multiple failures and has long been recognized (U.S. NRC, 1975) as an important issue in the Probabilistic Safety Assessment (PSA) and uncertainty and sensitivity analysis has the important information for the evaluation of system reliability. In this study, two cases has been considered, in the first case, author have made the analysis of reliability of PWR safety system by GO-FLOW methodology alternatively to Fault Tree Analysis and Even Tree because it is success-oriented system analysis technique and comparatively easy to conduct the reliability analysis of the complex system. In the second case, sensitivity analysis has been made in order to prioritize the important parameters which have largest contribution to system reliability and also for common cause failure analysis and uncertainty analysis. For an example of phased mission system, PWR containment spray system has been considered.

A k-out-of-N:G redundant system with cold standby units and common-cause failures

Traditional design of CPR1000 nuclear plants has a lot of studies on engineering safety facilities which are important for Design Basis Conditions (DBC). Based on more recent studies on safety, such as Defense in Depth (DID) concept, Probabilistic Safety Analysis (PSA), and especially after lessons from Fukushima Nuclear Accident, more and more attentions are paid to Design Extension Conditions (DEC). This paper has studied the abilities and requirements of CPR1000 Nuclear Plants for DEC accidents mitigation. Some important modifications on mechanical systems are introduced. DEC accidents are separated into DEC-A, and DEC-B. DEC-A accidents are caused by multiple failures. A main reason for a multiple failure is Common Cause Failure (CCF), such as CCF on Emergency Feed Water System (EFWS) pumps, Emergency Core Cooling System (ECCS) pumps, Emergency Diesel Generators or Cooling chain. Diverse design should be considered to deal with CCF. Several modifications are applied, such as enhancing EFWS turbine pumps, fast-backup between ECCS and Containment Spray System (CSS), mobile water makeup and mobile injection for primary and secondary side, etc. For DEC-B, which means severe accidents, several dedicated systems are added to CPR1000 design, such as hydrogen monitor and control, containment venting filter and In-Vessel Retention systems. Thanks to all the modifications, CPR1000 can sufficiently improve its defense for DEC accidents, which will help CPR1000 to reach a new safety level.

Common cause failures (CCFs) are an important part of reliability analysis when working with safety instrumented systems (SIS), and engineers have been aware of these types of failures since the midseventies (Fleming, 1974). The purpose of this paper is to develop a strategy for analyzing CCFs by studying an example of an oil-pressure system. This paper presents an example which the standard beta-factor model is unable to describe properly. The focus is given to the following three methods, the beta-factor model, the PDS method, and Markov analysis. The need for Markov analysis becomes evident when working with SIS of a more complex nature, for instance non-identical components. This method is, when using a computer, not overly complex and it is possible to model virtually every possible architecture a SIS may have. Finally, it is always important to remember that if there exists any feedback data or expert knowledge describing the distribution of the number of components that fail in a CCF, this is vital in deciding the most descriptive CCF model. By the term descriptive model, we mean a model that both describes the architecture of the system as accurately as possible, and also makes as few assumptions as possible. It is difficult to assess the quality of the results (since feedback data is missing), so faith is put into the model which mathematically resembles the SIS the most.

Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.

This paper presents two mathematical models representing three unit redundant systems with common cause failures and human errors. Reliability, steady state availability, mean time to failure (MTTF) and variance of time to failure formulas are developed for both models. Supplementary variables and Markov techniques were employed to obtain the resulting expressions. Reliability, availability, failure probability and MTTF plots are .shown

A novel approach for software reliability analysis of digital instrumentation and control systems in nuclear power plants