Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Khakzad, Nima; Khan, Faisal; Amyotte, Paul

doi:10.1016/j.psep.2012.01.005

Cited by 486 publications

(246 citation statements)

References 19 publications

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“…In a Bayesian network, the nodes represent variables, arcs signify direct causal relationships between the linked nodes, and CPTs assigned to the nodes denote conditional dependencies [9].…”

Section: Bayesian Network Predicting (Bnp) Methodsmentioning

confidence: 99%

Location predicting methods for UAVs

Peng¹

2017

AIP Conference Proceedings

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Abstract. Location prediction of unmanned aerial vehicle (UAV) is important for fighting with its enemy and ensuring its normal operation. This paper presents the motion model of UAVs and reduces the state space into 7 dimensions. The Bayesian Network, Markov Chain, Curve Fitting and Neural Network are introduced for designing predicting methods. Then Curve Fitting Predicting method, Markov Chain Predicting method, Bayesian Network Predicting method and Neural Network Predicting method are designed for UAVs. The simulation result shows that 1) Neural Network Predicting method has highest predicting accuracy; 2) Markov Chain Predicting method and Bayesian Network Predicting method methods have similar performance and both are better than Bayesian Network Predicting method methods; 3) Neural Network Predicting method is the first choice when predicting the locations of UAVs.

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Section: Bayesian Network Predicting (Bnp) Methodsmentioning

confidence: 99%

Location predicting methods for UAVs

Peng¹

2017

AIP Conference Proceedings

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“…BN takes advantage of Bayes theorem to update the prior occurrence probability of objects given new information, called evidence E, thus yielding the posteriors. This new information usually becomes available during the operational life of a system, including the occurrence or nonoccurrence of objects (Khakzad et al, 2012) Figure 2: Goal-directed task analysis for determining SA requirements (Endsley, 2006).…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

A situation risk awareness approach for process systems safety

Naderpour

Zhang

2014

Safety Science

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Abstract:Promoting situation awareness is an important design objective for a wide variety of domains, especially for process systems where the information flow is quite high and poor decisions may lead to serious consequences. In today"s process systems, operators are often moved to a control room far away from the physical environment, and increasing amounts of information are passed to them via automated systems, they therefore need a greater level of support to control and maintain the facilities in safe conditions. This paper proposes a situation risk awareness approach for process systems safety where the effect of ever-increasing situational complexity on human decision-makers is a concern. To develop the approach, two important aspects -addressing hazards that arise from hardware failure and reducing human error through decision-making -have been considered. The proposed situation risk awareness approach includes two major elements: an evidence preparation component and a situation assessment component. The evidence preparation component provides the soft evidence, using a fuzzy partitioning method, that is used in the subsequent situation assessment component. The situation assessment component includes a situational network based on dynamic Bayesian networks to model the abnormal situations, and a fuzzy risk estimation method to generate the assessment result. A case from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of the proposed approach.

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“…There are many algorithms applied to calculation of the probabilities of node accident and accident consequence of dynamic bow tie analysis: Hidden Markov Model, Bayesian Network (Khakzad et al 2013), Integral Algorithm (Amari et al 2003) and Monte Carlo Simulation (Kim et al 2016). In terms of simulated time and practicability, the Monte Carlo Simulation has an advantage over other methods.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Reliability Based Bow Tie Analysis of Coal Mine Water Inrush Risks

Zhang

Wang

Wei

2017

GREE

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Water inrush is a serious geological accident in underground coal mines. This paper focuses on the method of the bow tie analysis based on fuzzy reliability to assess the risk of water inrush in coal mines. First, post failure probability and repair probability of initiating events are divided into 5 levels. Then triangular fuzzy numbers are adopted to quantify experts' language and to acquire the reliability rate which close to the actual values by defuzzification and calculation. According to dynamic bow-tie analysis model, using the method of moments to fit the reliability time of events by Weibull distribution and offering the random sampling rule of failure and repair time for illustrating the feasibility of fuzzy reliability evaluation. To demonstrate the effectiveness of the proposed assessment approach, bow tie analysis model of water inrush in coal mines is taken as an example.

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Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network

Cited by 486 publications

References 19 publications

Location predicting methods for UAVs

Location predicting methods for UAVs

A situation risk awareness approach for process systems safety

Fuzzy Reliability Based Bow Tie Analysis of Coal Mine Water Inrush Risks

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