Common-cause Failures Research Articles

Reliability analysis of load-sharing system with the common-cause failure based on GO-FLOW method

The load-sharing system (LSS) with the common-cause failure (CCF) is widely used in industrial engineering applications. If a component in this system fails, the total load is shared by the other components, leading to an increased failure rate of the surviving components. The traditional GO-FLOW method is difficult to calculate the reliability of this system accurately. To address this issue, a new reliability analysis approach is proposed in this paper. In this approach, a new GO-FLOW operator is established to simulate the LSS with CCF. Firstly, the state transfer relationship between components in the LSS is identified. Secondly, the α-factor is used to establish the relationship between the independent failure rate λI and the CCF rate λC. Finally, the Markov method is employed to calculate the transient-state and steady-state reliability of the system, and the calculation process for the parallel system and k-out-of-n(F) system are given, respectively. The feasibility of the proposed method is illustrated through a numerical example of a distributed electric propulsion system. This approach extends the applicability of the GO-FLOW method.

Reliability Evaluation of Multi-State Solar Energy Generating System with Inverters Considering Common Cause Failures

To ensure the efficient functioning of solar energy generation systems, it is crucial to have dependable designs and regular maintenance. However, when these systems or their components operate at multiple working levels, optimizing reliability becomes a complex task for models and analyses. In the context of reliability modeling in solar energy generation systems, researchers often assume that random variables follow an exponential distribution (binary-state representation) as a simplification, although this may not always hold true for real-world engineering systems. In the present paper, a multi-state solar energy generating system with inverters in series configuration is investigated, in which unreliable by-pass changeover switches, common cause failures (CCFs), and multiple repairman vacations are also considered. Furthermore, the arrivals of CCFs and the repair processes of the failed system due to CCFs are governed by different Markovian arrival processes (MAPs), and the lifetimes and repair times of inverters and by-pass changeover switches and the repairman vacation time in the system have different phase-type (PH) distributions. Therefore, the behavior of the system is represented using a Markov process methodology, and reliability measures for the proposed system are derived utilizing aggregated stochastic process theory. Finally, a numerical example and a comparison analysis are presented to demonstrate the findings.

Methods for Estimating Parameters of the Common Cause Failure Model Based on Data with Uncertainty

In this paper, we propose a new method to deal with uncertain data in the context of Common Cause Failure (CCF) analysis. Uncertain CCF data refer to the data for which the number of components involved in the failure events is not exactly known. We introduce a new formalism to describe uncertain CCF data to avoid subjective probabilities for the number of failed components in each CCF event that are used in classical methods such as the impact vector method. The parameters of the [Formula: see text]-factor model are estimated using the maximum likelihood method relying on properties of the nested Dirichlet distribution and grouped Dirichlet distribution. A data augmentation technique with an expectation-maximization algorithm is also developed for some schemes of data with uncertainty. Finally, we evaluate the performance of the proposed method through numerical simulations and illustrate its application using an example from the literature.

Reliability analysis of digital reactor protection systems in floating nuclear power plants

Abstract This paper presents a reliability model for digital reactor protection systems (RPSs) in floating nuclear power plants (FNPPs) that accounts for both the internal characteristics of RPS and the external environment. The internal characteristics of RPS include independent failures and common-cause failures (CCFs) of components, repair behavior, and actuation logic degradation. For the external environment, we incorporated a parts-pressure method and used the environmental factors to describe the impact of marine environment at component level. Detailed Monte Carlo simulation (MCS) algorithm was proposed to solve the reliability models with different environmental factors, and the results showed that the maximum value of the environmental factor was 3.2 under the requirements that the probability for RPS failing to generate the trip signal does not exceed 1 × 10−5 and the spurious trip frequency does not exceed one time per year. Reliability indexes, such as failure probability and spurious trip frequency, were also derived. The 90 % confidence intervals of these two indexes were further calculated in the uncertainty analysis by using the kernel density estimation (KDE) approach.

Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters

Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault tree of CPU system is given to verify the effectiveness of the proposed framework.

Potential role of nuclear power in a carbon-free world

Thermal Power Plants (ThPP), along with transport facilities, are the major sources for industrial emissions of carbon dioxide (CO2) believed to be responsible for the greenhouse effect leading to overheating of the lower atmosphere. In the opinion of many scientists, there is a threshold value of the average atmospheric temperature exceeding, which entails the potential for the development of irreversible processes threatening the existence of humankind. To avoid this danger, governments in nearly 200 countries have chosen voluntarily to reach net-zero CO2 emissions by 2050. Renewable Energy Sources (RES), including wind and solar power plants, have been selected as substitutes for ThPPs. However, energy systems based on RES only need to be multiply redundant in terms of installed capacity due to their efficiency being heavily dependent on daily, seasonal and weather factors, leave alone the scale of the required material resources (metals, polymers, concrete, glass, etc.). The major drawback of such energy systems is, however, the RES common-cause failure, e.g., in the event of a global volcanic eruption, when no energy-security requirement can be met to provide energy for satisfying the most vital needs. A need is fully evident for furnishing such energy system with another power source to be not dependent on the event that has caused the mass RES failure. With the net-zero-carbon requirement taken into account, Nuclear Power (NP) appears to be the best option in this respect. Modern NP does not however fully suits this role due to its inherent drawbacks (limited fuel resources, pending Spent Nuclear Fuel (SNF) and RadioActive Wastes (RAW) handling and nuclear-material nonproliferation issues). A potential solution to these drawbacks is a two-component NP technology in a closed nuclear-fuel cycle currently in the process of development. In Russia, where the greatest progress has been achieved in this field of development, under construction is a pilot and demonstration energy complex with the BREST-OD-300 nuclear unit expected to be started up in 2026–2027. Another promising designs to be developed are Small Modular Reactors (SMRs) / Small Nuclear Power Plants (SNPPs).

Quantifying the impact of risk mitigation measures using SPAR-H and RCM Approaches: Case study based on VVER-1000 systems

Probabilistic Safety Assessment (PSA) provides a systematic and quantitative approach to assess the risk associated with different components and systems within a nuclear power plant. By quantifying the impact of risk mitigation measures, decision-makers can prioritize and implement solutions that have the most significant impact on reducing risk. Fault Tree Analysis (FTA) is employed as a systematic method within PSA, encompassing all modes of failure across equipment and associated support systems, including unavailability due to maintenance (UDM), human errors (HEs), and common cause failures (CCFs). In the present research, the Extra Borating System (EBS) and the Chilled Water Operating Circuit System (CWOCS) of essential loads are modeled in the SAPHIRE code using VVER-1000 design data. Through importance analysis employing the Fussell-Vesley criterion, the most significant basic events for these two systems are identified. Subsequently, the outcomes of the importance analysis are examined using diversity, SPAR-H, and Reliability Centered Maintenance (RCM) techniques. Recommendations such as the enhancement of diversity, increasing staff training, promoting Human Machine Interface (HMI), reduction of maintenance time, and improvement of power supply reliability are proposed for the most crucial basic events. The ensuing impact of these measures on the reliability of the EBS and CWOCS is subsequently assessed. Through a comparison between the new failure probability and the previous, it is determined that improvements in CCFs contribute a 30% effect; HEs have a 14% effect and UDMs have a 5% effect, with a cumulative impact of 35% on the failure probability of the EBS system. Similarly, for the CWOCS system, enhancements in CCFs contribute 20%, UDMs 17%, and improved power supply reliability 19%, with a cumulative impact of 29%.

PROBABILISTIC ANALYSIS OF A THREE-UNIT SERIES-PARALLEL SYSTEM WITH COMMON-CAUSE, HUMAN ERROR AND ENVIRONMENTAL STATE FAILURES

This paper presents a mathematical model for performing reliability and availability analyses of Series- Parallel system with constant human error and common-cause failure rates. The method of Integro-differential equation was used to develop equations for the model to obtain the general expression. The Laplace transform technique was used to obtain the system reliability with constant human error failure rate of the system

Exhaustive Test Case Generation for Nuclear Safety Software Based on the Software Logic Model

An issue regarding the incorporation of software reliability within the nuclear power plant (NPP) probabilistic risk assessment model has emerged in the licensing processes of digitalized NPPs. Since software failure induces common-cause failure of the processor modules, the reliability of the software used in the NPP safety-critical instrumentation and control systems must be quantified and verified with proper test cases and environments. In this study, a software testing method based on the minimal cut set (MCS)–based exhaustive test case generation scheme is proposed where the software logic model is developed from available information on the software development and the MCSs that represent the necessary and sufficient conditions for the software variables’ states to produce safety software outputs are generated. The MCSs are then converted into the test cases, which can be used as inputs to the test bed to verify that the test cases produce correct outputs after software execution. The effectiveness of the proposed method is demonstrated with the safety-critical trip logic software of the APR-1400 reactor protection system. The method provides a systematic way to conduct exhaustive software testing and prove the functionality of the nuclear safety software based on the test result without uncertainties.

Research Activities Associated with the Treatment of Potential Common-Cause Failures in Event and Condition Assessments

The U.S. Nuclear Regulatory Commission (NRC) and the nuclear power industry have evaluated and discussed various assumptions and methods for the treatment of potential common-cause failures (CCFs) in event and condition assessments (ECAs), specifically risk assessments performed as part of the NRC’s Significance Determination Process (SDP) in recent years. The basis for how a potential CCF is treated in SDP risk assessments is provided in NUREG-2225, “Basis for the Treatment of Potential Common-Cause Failure in the Significance Determination Process.” In light of new information and advancements in probabilistic risk assessment technology, the NRC and Idaho National Laboratory (INL) have continued the development of the causal alpha factor method (CAFM) for potential use in ECAs. The NRC and INL evaluated the suitability of using CAFM in SDP evaluations and reviewed the methodology to identify any potential data gaps. Furthermore, an investigation was performed on the practice of common-cause component groups to determine if changes are needed to ensure that CCF is appropriately accounted for in the Standardized Plant Analysis Risk models. In addition, a focused review of the existing method that estimates CCF parameters was performed to determine if the assumptions used in the existing process result in CCF parameters that are representative of current industry performance. It was also desired to gather a better understanding of the aspects of the alpha factor method and data calculation process that either have significant effects on the CCF parameters and/or increase the uncertainties associated with these parameters. This paper provides a summary of recently completed work, including insights, conclusions, and recommendations from this effort.

Study of Dynamic Solutions for Human–Machine System with Human Error and Common-Cause Failure

This work investigates a dynamic solution of human–machine systems with human error and common-cause failure. By means of functional analysis, it is proved that the semigroup generated by the underlying operator converges exponentially to a projection operator by analyzing the spectral property of the underlying operator, and the asymptotic expressions of the system’s time-dependent solutions are presented. We also provide numerical examples to illustrate the effects of different parameters on the system and the theoretical analysis’s validity.

A Novel Markov Model for Protective Relay Reliability

Microprocessor-based protective relay has risen to the ‘technology of choice’ with regard to power system protection due to its monitoring capability, self-checking capability, event recording capability, communication capability, capability for remote interrogation and setting application, and multiple setting groups. However, the existing Markov reliability model for protective relays does not reflect the state of the art of microprocessor-based relays. And the complexity of the Markov model limits its application in the wide range of relay studies. Therefore, this paper establishes the general state-transition diagrams of protected components and relays, and a novel Markov model of a protection/component system by taking account of monitoring, self-checking, routine tests, common-cause failures, human error, operation of back-up protection, relay failure to operations and relay mal-trips. The study results of the sensitivity analysis and comparison analysis demonstrate the rationality and superiority of the novel model.

Integrated unavailability analysis including test degradation and efficiency, components ageing and common cause failures

The article proposes an integrated model for the unavailability of standby components and systems, considering the impacts of ageing, test degradation and test efficiency on independent and common cause failures (CCF) probabilities. The results showed a considerable impact of test efficiency on systems reliability and risk.The CCF model was expanded to incorporate the additional contribution derived from the repair time of identical and redundant components. Two application cases, of system unavailability and Probabilistic Safety Assessment (PSA), were evaluated for several combinations of test efficiency, degradation and ageing, using the traditional and the expanded CCF models. Ignoring the contribution to CCFs derived from the repair time leads to significant risk underestimations, even when ageing and test degradation are not included.Great uncertainties of the model parameters represent an important restriction for the introduction of adverse test effects in risk analysis, but they are important contributors that must be considered explicitly.

Mathematical modelling of mission-abort policies: a review

Abstract This paper reviews works that consider the mathematical modelling of mission-abort policies (MAPs). In a MAP, a valuable, and perhaps, vulnerable system performs a mission with two, sometimes conflicting objectives, mission success and system survival; and the purpose of modelling is to determine the conditions under which a mission should be aborted. Such problems are important in defence and are emerging in transportation and health management. We classify models by the nature of the mission and the system, the nature of the return or rescue, type of deterioration model and the decision objectives. We show that the majority of works consider a model of a one system, one target mission in which the mission is aborted once the hazard of failure reaches a critical level, and the operating environment is the same for the outbound and inbound parts of the mission. Typically, the hazard of failure depends on the number of shocks received so far. Our analysis indicates that there has been little modelling development for multiple systems that can multi-task and for dependent systems with common-cause failures, for example. We find no evidence that MAPs are used in practice and no works reviewed develop software demonstrators. We think there is considerable scope for modelling applications in transportation (e.g. dynamic train re-scheduling and last-mile logistics) and medical treatments, and MAPs may be more general than the literature that we have reviewed suggests.

Assessing the Impact of Common Cause Failures on Site Risk within Level 1 Multi-Unit PSA

Common cause failures (CCFs) may lead to the simultaneous unavailability or failure of numerous components in the nuclear power plant because of the existence of a shared cause when an initiating event disrupts the normal functioning of nuclear power plants. The presence of common cause failures (intra-unit and inter-unit) can be recognized in a multi-unit probabilistic safety assessment (MUPSA) as a crucial dependency factor that can influence accident scenarios and the core damage frequency (CDF), as CCF may affect the availability and proper operation of mitigating systems. Since such failures are likely to significantly undermine the benefits of the concept of redundancy in nuclear power plant systems, it is necessary to identify the CCFs that contribute to the core damage in a multi-unit site and analyse their overall quantitative magnitude and qualitative proportions. In this study, a twin-unit generic pressurized water reactor (PWR) nuclear plant is modeled using the AIMS-PSA software. For the loss-of-offsite-power (LOOP) and station blackout (SBO) events, the site CDF was calculated, and the cut-sets produced by this quantification were examined for the modeled CCF basic events in the fault trees. The quantitative and qualitative contributions of the CCFs to the frequency of site core damage were examined. CCFs in the modeled fault trees contributed to 4.58% to the site CDF of the combined LOOP followed by SBO event. In the LOOP event alone that leads to core damage, the CCF contributed 4.58% to the site CDF while CCFs contributed 17.19% to the site CDF in the SBO event alone that leads to core damage. With CCF events considered in the modeling process, the site CDF estimated with CCF events increased by 7.53% in the combined LOOP followed by SBO event. In the LOOP event alone that leads to core damage, inclusion of CCF events in the modeling increased the site CDF by 7.42%. A 15.66% increase in site CDF was recorded in the SBO event alone that leads to core damage as compared to modeling without CCF events. The results show how crucial the common cause failure contribution is to site CDF. The safety of the nuclear plant at a site is impacted by an increase in site CDF when common cause failures are considered. The various CCF fundamental event compositions and their percentage contributions were explicitly examined by the minimal cut-sets which leads to core damage in the units. In conclusion, this study’s findings can help us better understand how CCFs increase multi-unit site risk and can also act as a starting point for future studies on the qualitative and quantitative categorizations of CCF effects within MUPSA.

Reliability analysis and optimization for a redundant system with dependent failures and variable repair rates

This paper considers the reliability analysis and the bi-objective optimal problem for a redundant system with two dependent failures: common cause failure (CCF) and load-sharing failure, where the system contains N active components, W warm standbys and C cold standbys, a K-mixed redundancy strategy is considered. In the system, there is a repair team providing variable repair rates according to differential situations: when there are standby components available in the system, the repair team assigns a regular repairman to repair the failed components, with the known fact that regular repairman is not as skilled as an expert repairman in doing some complex repair; when all the spares are exhausted, the repair team assigns an expert repairman to attend failed components; when the system breaks down due to a CCF’s occurrence, the repair team provides a very special repair service to restore the system to normal working state. The steady-state distribution of the system is obtained by matrix-analytic method. The sets of equations satisfied by the reliability functions and transient availabilities from arbitrary initial state are respectively presented and solved by Markov renewal theory, Laplace–Stieltjes transform (LST) and Laplace transform (LT) technique. Based on the obtained results, sensitivity analysis is performed by some numerical experiments. From economic view point, the bi-objective optimization problem is constructed and solved by NSGA-II algorithm for the minimal expected total cost and maximal availability.

Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

• A comprehensive quantitative evaluation of common cause failures is performed for safety-critical digital control systems; • Both hardware and software common cause failures are tracked and identified; • Failure probabilities of digital control systems are estimated via a multiscale reliability analysis approach. • The impact of common cause failures to plant safety is evaluated. Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

Sensitivity Analysis for a Redundant System with Random Inspection and R Repairmen Under Repair Pressure

This study examines a repairable K -out-of- N + W : G system with warm standby components and R repairmen, where common cause failure (CCF), random inspection, and a repair pressure coefficient are involved. Due to the typical non-self-announcement of the standby component’s failure, performing a random inspection on warm standby components is necessary to find whether there are failed standby components. When the number of failed components is larger than R (heavy loading occurs), all the repairmen increase their repair rates under a repair pressure coefficient to reduce the waiting queue and enhance the reliability and availability of the system. By adopting a matrix-analytic method and Markov renewal theory, this study provides the steady-state distribution and some reliability indices mean time to first failure time (MTTFF) and reliability function from the arbitrary initial state. By some numerical experiments, sensitivity analysis is finally given to show the effect of the system parameters on some performance measures.

Operational Resilience of Network Considering Common-cause Failures

Operational Resilience of Network Considering Common-cause Failures

Compensated Feed water Pump Control Analysis of Effect of Base-Load Electricity Demand Reduction on Nuclear Steam Supply System

The effect of compensated feedwater (FW) pump control on a nuclear steam supply system with a significant reduction of baseload electricity demand as a common-cause failure could result in temperature elevation of the reactor coolant system and corresponding pressure increases in the pressurizer and steam generators above the set points. The shutting and opening of the pressure relief valve causes the fluid flow rate to transition from laminar to turbulence flow, where a sudden burst, chaotic movement, and inertial forces and weight of the fluid have the potential to cause a break in pipelines leading to a loss-of-coolant accident. This study employs the Fourier transform to simulate the impact of force as the power spectral density (in dBm/Hz) measured in 1 to 99 label harmonics over a specified time window using MATLAB/Simulink library tools. The experimental results show that compensated FW pump control could significantly reduce the effect of turbulence and reveal a perturbation settlement state prior to steady-state laminar flow.