Identifying emerging failure phenomena in complex systems through engineering data mapping

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

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.

The complexity of problems facing society continues to grow, and decision-makers and problem-solvers are finding many of today's emerging problems to be beyond their capability to adequately address. There is agreement in the literature that problems of this nature are complex system problems, inextricably linked to some highly complex system of systems. Establishing a clear understanding of the specific complex system context is fundamental to the process of understanding and analyzing complex systems and complex system problems across all of the different systems-based disciplines. While complex system context is widely referred to in systems literature, there is no clear characterization of exactly what system context is, making this foundational system concept ambiguous. This research addressed this gap in the systems body of knowledge by providing the needed detail and clarity to the concept of complex system context. A rigorous research methodology, employing the grounded theory method, was used to analyze data collected through a series of semi-structured interviews conducted with individuals reflecting a wide range of systems education and practical experience. Two research questions were identified as integral to increasing the understanding of context within complex systems. (1) What are the constituent elements of complex system context, and what attributes and dimensions characterize these elements? (2) What systems-based framework can be developed for constructing and articulating complex system context? Using the grounded theory method, a theory of system context was constructed, adding to the systems body of knowledge and substantiating a comprehensive and unambiguous theoretical construct for system context within complex systems. Then, based on this theory, a conceptual model to articulate and capture system-specific complex system context was developed---the Complex System Contextual Framework (CSCF). The CSCF shows significant promise for contribution to systems practitioners by supporting the future development of tools to help practitioners capture system context as a part of complex system problem formulation. The research also made a contribution in the area of research methodologies by furthering the use of the grounded theory method in the engineering management and systems engineering domain, an area where its application has been very limited.

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.

A pragmatic approach to modeling common cause failures in multi-unit PSA for nuclear power plant sites with a large number of units

Estimation of the common cause failure probabilities on the component group with mixed testing scheme

Reliability assessment of complex engineered systems is challenging as epistemic uncertainty and common cause failure (CCF) are inevitable. The probabilistic common cause failure (PCCF), which characterizes the simultaneous failures of multiple components with distinguished chances, is a generalized model of traditional CCF model. To accurately assess system reliability, it is of great significance to take both the effects of PCCF and the epistemic uncertainty of components’ state probabilities into account. In this paper, an evidential network model is proposed to assess system reliability with interval-valued PCCFs and epistemic uncertainty associated with components’ state probabilities. The procedures of computing the mass distribution of a component suffering from multiple PCCFs are detailed. The inference algorithm in the evidential network is, then, used to calculate the mass distribution of the entire system. The Birnbaum importance measure is also defined to identify the weak components under PCCFs and epistemic uncertainty. A safety instrumented system is exemplified to demonstrate the effectiveness of the proposed evidential network model in terms of coping with PCCFs and epistemic uncertainty. The importance results show that both the epistemic uncertainty associated with components’ state probabilities and PCCFs have impact on components’ importance.

This paper analyses the problem of epistemic uncertainty in assessing the performance of safety instrumented systems (SIS) using fault trees. The imperfect knowledge concerns the common cause failure (CCF) involved in the SIS in low demand mode. The point-valued CCF factors are replaced by fuzzy numbers, allowing experts to express their uncertainty about the CCF values. This paper shows how these uncertainties propagate through the fault tree and how this induces an uncertainty to the values of the SIS failure probability on demand and to the safety integrity level of the SIS. For the sake of verification and comparison, and to show the exactness of the approach, a Monte Carlo sampling approach is proposed, where by a uniform or triangular second-order probability distribution of CCF factors is considered.

Birnbaum importance measures the significance of a component to the system reliability. And it is widely applied in system reliability designs and risk assessments. Some studies have been made on Birnbaum importance for k-out-of-n systems, but most of them assumed that the failures of components are statistically independent of each other. In practice, component failures are usually statistically dependent and common cause failures (CCF) are almost ubiquitous in complex systems. Based on the system-level stress-strength interference (SSI) analysis, Birnbaum importance for k-out-of-n systems with same components and different components are extended. This approach can be applied to dependent systems to find weaknesses of systems in the presence of CCFs. Through two examples, we further illustrate the importance measure and discuss some attractive properties about the new importance measure: i) the extended importance measure is intuitive and simple to use; ii) importance of a component changes regularly with the variation of strength distribution parameters of the component; iii) failure dependence has a great influence on the importance evaluation and further may results in wrong importance ranking of all the components.

The redundancy allocation problem is formulated with the objective of maximizing system reliability in the presence of common cause failures. These types of failures can be described as events that cause simultaneous failure of multiple components due to a common cause. The redundancy allocation problem involves the optimal selection of redundancy levels and combination of components to maximize reliability given cost and weight constraints. This new problem formulation offers several distinct benefits compared to traditional problem formulations. For some systems, recognition of common cause failure events is necessary so that the overall system reliability estimation, and the associated design, correspond to the true system reliability behavior in a more realistic way. This research study represents one of the first efforts to analyze the mixing of components, together with the inclusion of common cause failure events, to develop optimal solutions to the redundancy allocation problem. A non-linear optimization model is presented. Solutions to an illustrative problem are obtained to demonstrate consideration of common cause failures will lead to different and preferred “optimal” design strategies.

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.

Common cause failures in safety instrumented systems on oil and gas installations: Implementing defense measures through function testing

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.

In quantitative analysis of common cause failures (CCFs), importance measures for ranking the importance of the CCF events will be significant to limit the CCF analysis framework, find out efficient defence strategies against CCF, and make sensitivity analysis for them. In this paper, three importance measures are defined for different types of common cause failure events: Structure Importance, Probability Importance, and β-Importance. Simplified algorithms for calculating these importance measures are developed.