Dynamic availability assessment of safety critical systems using a dynamic Bayesian network

Amin, Md. Tanjin; Khan, Faisal; Imtiaz, Syed

doi:10.1016/j.ress.2018.05.017

Cited by 110 publications

(32 citation statements)

References 26 publications

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“…[16,39] The basic and intermediate events considered (Table A1 in the Appendix section) and their failure probabilities were collected from the references. [16,39,42,43] The BNs for these safety barriers are also shown in the Appendix section ( Figures A1-A7). The safety barrier failure probabilities estimated through the BNs are listed in Table 4.…”

Section: Application Of the Proposed Methodology 41 | A Binary Dismentioning

confidence: 99%

A novel data‐driven methodology for fault detection and dynamic risk assessment

et al. 2020

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This paper presents a novel methodology for dynamic risk analysis, integrating the multivariate data-based process monitoring and logical dynamic failure prediction model. This concept for dynamic risk analysis is comprised of the fault assessment and dynamic failure prognosis modules. A combination of the naïve Bayes classifier, Bayesian network, and event tree analysis is utilized to manifest the concept. The naïve Bayes classifier is used for fault detection and diagnosis; it also generates a multivariate probability for a fault class in each time-step, which is used for dynamic failure prognosis by different paths a fault can lead a process to failure. The proposed framework has been applied to two process systems: a binary distillation column and the RT 580 experimental setup in four fault scenarios, and it is found the developed technique can effectively monitor the process and predict the failure.

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Section: Application Of the Proposed Methodology 41 | A Binary Dismentioning

confidence: 99%

A novel data‐driven methodology for fault detection and dynamic risk assessment

et al. 2020

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“…As part of their extensive literature review on the use of Bayesian networks for fault diagnostics, Cai et al [15] found that more recent research used these fundamental reliability relationships to pursue specific areas of reliability engineering research, including process, structural, and manufacturing systems. Amin et al [16] used DBNs to determine a dynamic availability assessment of safety critical systems, while Wu [17] found that DBNs could be used to make safety decisions for tunnel constructions. Rebello et al [18] relied on hidden Markov models (HMMs) to monitor system functionality through DBNs.…”

Section: Dynamic Bayesian Network and Related Researchmentioning

confidence: 99%

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Lewis

Groth

2020

Algorithms

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Dynamic Bayesian networks (DBNs) represent complex time-dependent causal relationships through the use of conditional probabilities and directed acyclic graph models. DBNs enable the forward and backward inference of system states, diagnosing current system health, and forecasting future system prognosis within the same modeling framework. As a result, there has been growing interest in using DBNs for reliability engineering problems and applications in risk assessment. However, there are open questions about how they can be used to support diagnostics and prognostic health monitoring of a complex engineering system (CES), e.g., power plants, processing facilities and maritime vessels. These systems’ tightly integrated human, hardware, and software components and dynamic operational environments have previously been difficult to model. As part of the growing literature advancing the understanding of how DBNs can be used to improve the risk assessments and health monitoring of CESs, this paper shows the prognostic and diagnostic inference capabilities that are possible to encapsulate within a single DBN model. Using simulated accident sequence data from a model sodium fast nuclear reactor as a case study, a DBN is designed, quantified, and verified based on evidence associated with a transient overpower. The results indicate that a joint prognostic and diagnostic model that is responsive to new system evidence can be generated from operating data to represent CES health. Such a model can therefore serve as another training tool for CES operators to better prepare for accident scenarios.

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“…To establish a comprehensive risk assessment framework for gas pipeline accidents in a utility tunnel, the present article used a BT diagram to identify potential hazards and possible accident scenarios and applied a BN for dynamic quantitative risk analysis of gas pipeline accidents in a utility tunnel. Compared with conventional risk analysis methods, BN has proven to be effective for capturing and integrating qualitative and quantitative information from various sources and can facilitate accident scenario modeling with multistate variables [13,21,22], in particular for dynamic risk analysis [23][24][25][26]. The BN for the gas pipeline accident in a utility tunnel is transferred from the BT diagram through a developed mapping diagram.…”

Section: Introductionmentioning

confidence: 99%

Quantitative risk assessment of a natural gas pipeline in an underground utility tunnel

Fang

Bai

et al. 2019

Process Safety Progress

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With rapid urbanization in China, many underground utility tunnels have been established these years. This huge underground construction facilitates city life, but may introduce societal risks due to the installation of high-risk pipelines. Natural gas pipelines have the potential to cause catastrophic accident if a gas leakage and a subsequent explosion occurs. The potential hazards in the gas compartments of a utility tunnel are quite different from those in conventional directly buried gas pipelines. This study developed a dynamic quantitative risk analysis method for natural gas pipelines in a utility tunnel. First, potential accident scenarios of natural gas pipelines situated in a utility tunnel were identified and implemented in a Bow-tie diagram based on case studies of typical gas pipeline accidents and expert experience. Then, a Bayesian network was established from the Bow-tie diagram using a mapping algorithm. Based on a comprehensive analysis of the results of probability updating and sensitivity analysis, critical influencing factors were identified. The proposed framework provides a predictive analysis of the gas pipeline accident evolution process from causes to consequences and examines key challenges in gas pipeline risk management in utility tunnels.where P (NM) is the probability of "Near Miss", P (VS-good) represents the probability of good "Ventilation System", and P (VS-poor) indicates the Probability of poor "Ventilation System".

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Dynamic availability assessment of safety critical systems using a dynamic Bayesian network

Cited by 110 publications

References 26 publications

A novel data‐driven methodology for fault detection and dynamic risk assessment

A novel data‐driven methodology for fault detection and dynamic risk assessment

A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems

Quantitative risk assessment of a natural gas pipeline in an underground utility tunnel

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