Procedures for treating common cause failures in safety and reliability studies: Analytical background and techniques

Mosleh, Ali; Fleming, K.N.; Parry, Gareth; Paula, H.M.; Worledge, D.H.; Rasmuson, D.M.

doi:10.2172/6564737

Cited by 56 publications

(18 citation statements)

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“…These components usually share one or more of common factors, such as the same design, the same hardware, the same function, the same installation, maintenance, or operations staff, the same procedure, the same system/component interface, the same location, and the same environment. 41 The underlying causalities can be modeled and quantified by probabilistic causal models (including Bayesian networks, influence diagrams) and simulation models. 42 The CCF parameters may be estimated "by using a combination of empirical historical experience (databases), service factors, and engineering judgment".…”

Section: Cpccf Modelingmentioning

confidence: 99%

“…42 The CCF parameters may be estimated "by using a combination of empirical historical experience (databases), service factors, and engineering judgment". 43 Specifically, various parametric models have been proposed, including Basic Parameter Model, 44 Beta-Factor Model, 45 Alpha-Factor Model, 46 Multiple Greek Letter model, 47 Binomial Failure Rate model 48 and Multinomial Failure Rate model. 49 This work only focuses on the system-level reliability analysis; failure events are assumed to have different distributions with known parameters to demonstrate the flexibility of the proposed method.…”

Section: Cpccf Modelingmentioning

confidence: 99%

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Analysis of cascading probabilistic common cause failures

Wang

Lie

Xing

et al. 2023

Quality & Reliability Eng

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Common cause failures may contribute significantly to the reliability of a system and has attracted considerable research attentions. However, the existing models often assumed deterministic failures of components affected by the common cause (CC) and no cascading effects. This paper contributes by modeling cascading probabilistic common cause failures (CPCCFs), where a CC may cause multiple system components to fail with different probabilities and some components may fail in a domino chain manner. An explicit, analytical method is proposed for the reliability analysis of systems subject to CPCCFs, which may be triggered by external or internal CCs. The complex cascading effects are modeled using a directed acyclic graph structure. The proposed method has no limitation on the component time‐to‐failure distributions. Applications and advantages of the proposed method are illustrated through a detailed case study of a safety instrumented system for oil and gas transportation. Correctness of the proposed method is verified by Monte Carlo simulations. The time and space complexity of the method is also studied.

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Section: Cpccf Modelingmentioning

confidence: 99%

Section: Cpccf Modelingmentioning

confidence: 99%

Analysis of cascading probabilistic common cause failures

Wang

Lie

Xing

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Common Cause Analysis (CCA) is a set of safety analysis techniques firstly introduced in (Mosleh et al, 1989) and later employed in assessment procedures found in various industries, such as the nuclear and aerospace. In general, CCA allows to investigate a system architecture for common cause failure events, which are considered to be any component failures or external events that act as root causes for multiple failures occurred in other system components.…”

Section: Common Cause Analysismentioning

confidence: 99%

Model-Connected Safety Cases

Retouniotis

Papadopoulos

Sorokos

et al. 2017

Lecture Notes in Computer Science

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Regulatory authorities require justification that safety-critical systems exhibit acceptable levels of safety. Safety cases are traditionally documents which allow the exchange of information between stakeholders and communicate the rationale of how safety is achieved via a clear, convincing and comprehensive argument and its supporting evidence. In the automotive and aviation industries, safety cases have a critical role in the certification process and their maintenance is required throughout a system's lifecycle. Safety-case-based certification is typically handled manually and the increase in scale and complexity of modern systems renders it impractical and error prone.Several contemporary safety standards have adopted a safety-related framework that revolves around a concept of generic safety requirements, known as Safety Integrity Levels (SILs). Following these guidelines, safety can be justified through satisfaction of SILs. Careful examination of these standards suggests that despite the noticeable differences, there are converging aspects. This thesis elicits the common elements found in safety standards and defines a pattern for the development of safety cases for cross-sector application. It also establishes a metamodel that connects parts of the safety case with the target system architecture and model-based safety analysis methods. This enables the semiautomatic construction and maintenance of safety arguments that help mitigate problems related to manual approaches. Specifically, the proposed metamodel incorporates system modelling, failure information, model-based safety analysis and optimisation techniques to allocate requirements in the form of SILs. The system architecture and the allocated requirements along with a user-defined safety argument pattern, which describes the target argument structure, enable the instantiation algorithm to automatically generate the corresponding safety argument. The idea behind model-connected safety cases stemmed from a critical literature review on safety standards and practices related to safety cases. The thesis presents the method, and implemented framework, in detail and showcases the different phases and outcomes via a simple example. It then applies the method on a case study based on the Boeing 787's brake system and evaluates the resulting argument against certain criteria, such as scalability. Finally, contributions compared to traditional approaches are laid out.

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“…The methods used in the analysis need to be consistent with the state of the art and current best practices as defined in national and international PRA standards and guidance. In recent years, there have been a number of activities to develop PRA standards [Mosleh et al 1989]. The aim has been to improve the accuracy, consistency and usability of the PRAs produced [USNRC 1978].…”

Section: Pra Technical Acceptabilitymentioning

confidence: 99%

“…The cause determination should identify the cause of the failure or degraded performance to the extent that corrective action can be identified that would preclude the problem or ensure that it is anticipated prior to becoming a safety concern. It should address failure significance, the circumstances surrounding the failure or degraded performance, the characteristics of the failure, and whether the failure is isolated or has generic or common cause implications [Mosleh et al 1989].…”

Section: Impact Of the Legislative Context On Decision-makingmentioning

confidence: 99%

Overview of risk-informed decision-making processes

Zio¹,

Pedroni²

2012

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The authors introduce the general concepts, definitions and issues related to the use of Risk-informed decision-making (RIDM). These are structured processes which assist decision-makers when faced with high impact, complex decisions involving multiple objectives and the presence of uncertainty. They aim to ensure that decisions between competing alternatives are taken with an awareness of the risks associated with each option, and that all attributes of a decision are considered in an integrated manner. Motivations for the use of these techniques as a complement to more traditional deterministic approaches to risk assessment are provided. The RIDM processes adopted by NASA and by the US Nuclear Regulatory Commission are described in detail, with an analysis of commonalities and differences in approach.

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Procedures for treating common cause failures in safety and reliability studies: Analytical background and techniques

Cited by 56 publications

References 0 publications

Analysis of cascading probabilistic common cause failures

Analysis of cascading probabilistic common cause failures

Model-Connected Safety Cases

Overview of risk-informed decision-making processes

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