Capturing cognitive causal paths in human reliability analysis with Bayesian network models

Zwirglmaier, Kilian; Straub, Daniel; Groth, Katrina M.

doi:10.1016/j.ress.2016.10.010

Cited by 61 publications

(30 citation statements)

References 33 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However if this information is not expected to be available, these variables should be eliminated from the network to make the BN applicable in practice. In (Zwirglmaier et al, 2015) we use node removal algorithms for this purpose, as proposed in (Shachter, 1988). Straub and Der Kiureghian (2010) applied these algorithms to remove continuous variables in their enhanced BN framework.…”

Section: Unquantifiable Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Approaches to Bayesian network structure elicitation

Zwirglmaier¹,

Straub²

2016

Risk, Reliability and Safety: Innovating Theory and Practice

View full text Add to dashboard Cite

Bayesian networks (BN) are increasingly popular as a modeling tool for engineering risk and reliability analysis. BNs consist of two parts: the qualitative dependence structure represented by a directed acyclic graph (DAG), and the quantification of local dependences through conditional probability distributions. We review the qualitative modeling, the elicitation of the BN structure, and classify existing approaches into four categories: (1) the transformation from existing probabilistic models such as fault trees, event trees; (2) the derivation from existing physical or empirical models; (3) structure elicitation based on domain expert knowledge; (4) structure elicitation based on data. This paper gives general descriptions of these structure elicitation approaches and discusses how they are applied in the field of engineering risk analysis. The paper ends with a discussion of the challenges in BN structure elicitation.

show abstract

Section: Unquantifiable Variablesmentioning

confidence: 99%

“…6 (adapted from Zwirglmaier et al (2015)). Some of the parameters are not quantifiable with feasible effort.…”

Section: Application Examplementioning

confidence: 99%

See 1 more Smart Citation

Approaches to Bayesian network structure elicitation

Zwirglmaier¹,

Straub²

2016

Risk, Reliability and Safety: Innovating Theory and Practice

View full text Add to dashboard Cite

show abstract

“…Combined with the analytic hierarchy process, D numbers are also used to implement dependence assessment in HRA . In addition, as a powerful tool for HRA, Bayesian networks (BNs) have been used in many fields . Mkrtchyan, Podofillini have systematically reviewed the BN applications and gaps in HRA.…”

Section: Introductionmentioning

confidence: 99%

A method of human reliability analysis and quantification for space missions based on a Bayesian network and the cognitive reliability and error analysis method

Chen

Zhou

Lyu

et al. 2018

Quality & Reliability Eng

View full text Add to dashboard Cite

Analyses of human reliability during manned spaceflight are crucial because human error can easily arise in the extreme environment of space and may pose a great potential risk to the mission. Although various approaches exist for human reliability analysis (HRA), all these approaches are based on human behavior on the ground. Thus, to appropriately analyze human reliability during spaceflight, this paper proposes a space-based HRA method of quantifying the human error probability (HEP) for space missions. Instead of ground-based performance shaping factors (PSFs), this study addresses PSFs specific to the space environment, and a corresponding evaluation system is integrated into the proposed approach to fully consider space mission characteristics. A Bayesian network is constructed based on the cognitive reliability and error analysis method (CREAM) to model these space-based PSFs and their dependencies. By incorporating the Bayesian network, the proposed approach transforms the HEP estimation procedure into a probabilistic calculation, thereby overcoming the shortcomings of traditional HRA methods in addressing the uncertainty of the complex space environment. More importantly, by acquiring more information, the HEP estimates can be dynamically updated by means of this probabilistic calculation. By studying 2 examples and evaluating the HEPs for an International Space Station ingress procedure, the feasibility and superiority of the developed approach are validated both mathematically and in a practical scenario.

show abstract

“…Ren et al [15] put forward the multi-attribute group decision-making (MAGDM) with the integration of techniques for order preference by similarity to ideal solution (TOPSIS) and interval-valued intuitionistic fuzzy sets, which can consider the reliability of information. Zwirglmaier et al [16] put forward to adopt Bayesian model to capture cognitive causal relation in reliability investigation, and it successfully provided a potential tool for assessing error distribution. However, the Bayesian theory depends on a subjective judgment which brings inaccurate product optimization design plans, and fuzzy mathematics needs to make their membership functions be known in advance which is almost impossible for designers.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Zhang

Feng

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Featured Application: By integrating fuzzy reasoning Petri Nets, interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting, the proposed reliability-based and cost-oriented product optimization method is effective to conduct the early product design. A reasonable reliability optimization design scheme can be output, and it is of important guiding significance for the product detailed design. The superiority of the proposed method lies in its outstanding ability to deal with the uncertainties information and dynamic product failure propagation. Abstract:In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN), interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC) machine tool.

show abstract

Capturing cognitive causal paths in human reliability analysis with Bayesian network models

Cited by 61 publications

References 33 publications

Approaches to Bayesian network structure elicitation

Approaches to Bayesian network structure elicitation

A method of human reliability analysis and quantification for space missions based on a Bayesian network and the cognitive reliability and error analysis method

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Contact Info

Product

Resources

About