Article2016

Predictive Abnormal Events Analysis Using Continuous Bayesian Network

Guozheng Song · Faisal Irshad Khan · Ming Yang · Hangzhou Wang

9 citations0/0 files storedASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering

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Risk and Safety Analysis Reliability and Maintenance Optimization Bayesian Modeling and Causal Inference

Abstract

The reliable prediction and diagnosis of abnormal events provide much needed guidance for risk management. The traditional Bayesian network (traditional BN) has been used to dynamically predict and diagnose abnormal events. However, its inherent limitation caused by discrete categorization of random variables degrades the assessment reliability. This paper applied a continuous Bayesian network (CBN)-based model to reduce the above-mentioned limitation. To compute complex posterior distributions of CBN, the Markov chain Monte Carlo method (MCMC) was used. A case study was conducted to demonstrate the application of CBN, based on which a comparative analysis of the traditional BN and CBN was presented. This work highlights that the use of CBN can overcome the drawbacks of traditional BN to make dynamic prediction and diagnosis analysis more reliable.

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References

2001Improving the analysis of dependable systems by mapping fault trees into Bayesian networks 2012Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network 2013Quantitative risk analysis of offshore drilling operations: A Bayesian approach 2012Dynamic risk analysis using bow-tie approach 2014Bayesian-network-based safety risk analysis in construction projects 2006Plant-specific dynamic failure assessment using Bayesian theory 2014Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks 2012Precursor-based hierarchical Bayesian approach for rare event frequency estimation: A case of oil spill accidents

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