Research Papers

Predictive Abnormal Events Analysis Using Continuous Bayesian Network

[+] Author and Article Information
Guozheng Song, Ming Yang, Hangzhou Wang

Centre for Risk,
Integrity and Safety Engineering (C-RISE),
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X5, Canada

Faisal Khan

Centre for Risk,
Integrity and Safety Engineering (C-RISE),
Faculty of Engineering and Applied Science,
Memorial University of Newfoundland,
St. John's, NL A1B 3X5, Canada
e-mail: fikhan@mun.ca

1Corresponding author.

Manuscript received May 10, 2016; final manuscript received September 19, 2016; published online June 13, 2017. Assoc. Editor: Siu-Kui Au.

ASME J. Risk Uncertainty Part B 3(4), 041004 (Jun 13, 2017) (7 pages) Paper No: RISK-16-1082; doi: 10.1115/1.4035438 History: Received May 10, 2016; Revised September 19, 2016

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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Grahic Jump Location
Fig. 1

Procedure to convert traditional BN into CBN

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Fig. 2

The process to obtain continuous nodes of CBN

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Fig. 4

FT for severe roll

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Fig. 5

The traditional BN for severe roll

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Fig. 6

CBN for vessel roll

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Fig. 7

Distribution density of roll angles over different wind speeds: (a) wind speed 1 m/s, (b) wind speed 9.5 m/s, and (c) wind speed 11 m/s

Grahic Jump Location
Fig. 8

Distribution density of wave heights: (a) roll angle 1 deg, (b) roll angle 9.5 deg, (c) roll angle 10.5 deg, and (d) roll angle 30 deg




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