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research-article

Predictive Abnormal Events Analysis using Continuous Bayesian Network

[+] Author and Article Information
Guozheng Song

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's NL, Canada A1B 3X5
gs1870@mun.ca

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, Canada A1B 3X5
fikhan@mun.ca

Ming Yang

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

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, Canada A1B 3X5
hangzhouw@mun.ca

1Corresponding author.

ASME doi:10.1115/1.4035438 History: Received May 10, 2016; Revised September 19, 2016

Abstract

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

Copyright (c) 2016 by ASME
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