Journal ArticleDOI
A data-driven Bayesian network model integrating physical knowledge for prioritization of risk influencing factors
Huixing Meng,Xu An,Jinduo Xing +2 more
Reads0
Chats0
TLDR
Wang et al. as mentioned in this paper proposed a data-driven Bayesian Network (BN) model integrating physical information for risk analysis, which combined prior knowledge with structure learning and parameter learning to obtain a BN model.Abstract:
The coupling of multiple factors stemming from propagation effects and interdependency relationships among risks is prone to generate major accidents. It is of necessity to develop a feasible model with limited cases, which can generate reliable causal relationship evolution. To prioritize risk-influencing factors (RIFs) and investigate their relationships, we proposed a data-driven Bayesian Network (BN) model integrating physical information for risk analysis. Based on collected data, we combined prior knowledge with structure learning and parameter learning to obtain a BN model. In structure learning, we compared three structure learning algorithms including Bayesian search (BS), greedy thick thinning (GTT), and PC algorithm to obtain a robust directed acyclic graph (DAG). In parameter learning, we selected the expectation maximization (EM) algorithm to quantify the dependence and determine the probability distribution of node variables. This study provides a method to capture crucial factors and their interdependent relationships. To illustrate the applicability of the model, we developed a data-driven BN by taking the blowout accident as the case study. Eventually, we introduced vulnerability and resilience metrics for prioritizing risks through network propagation to conduct emergency plans and mitigation strategies . read more
Citations
More filters
Journal ArticleDOI
A dynamic quantitative risk assessment method for drilling well control by integrating multi types of risk factors
TL;DR: In this article , the authors present a dynamic quantitative risk assessment method for drilling well control by integrating multi types of risk factors, such as human errors, equipment failure, and internal mechanisms.
Journal ArticleDOI
A Novel Deep Learning Model based on Target Transformer for Fault Diagnosis of Chemical Process
TL;DR: In this article , the authors proposed a modified transformer model called Target Transformer, which includes not only a self-attention mechanism, but also a target attention mechanism for chemical process fault diagnoses.
Journal ArticleDOI
Large-scale chemical process causal discovery from big data with Transformer-based deep learning
TL;DR: In this paper , a causal discovery method based on the causality-gated time series Transformer (CGTST) is proposed to address the challenge of chemical process big data often exhibit nonlinearity and nonstationarity, and contain various forms of noise.
Journal ArticleDOI
An approach towards the implementation of a reliable resilience model based on machine learning
TL;DR: In this article , the authors proposed a systematic framework based on system engineering and focused on the reliability of the learning process of the Hidden Markov Model (HMM) coupled with the Baum-Welsh algorithm.
Journal ArticleDOI
Accident analysis and risk prediction of tank farm based on Bayesian network method
TL;DR: In this article , a novel accident analysis approach based on Bayesian networks (BNs) was proposed to achieve quantitative accident analysis and dynamic risk prediction of accident types and consequences, and the established BN model was applied to the accident occurred in Huangdao, China.
References
More filters
Journal ArticleDOI
A survey of cross-validation procedures for model selection
Sylvain Arlot,Alain Celisse +1 more
TL;DR: In this paper, a survey on the model selection performances of cross-validation procedures is presented, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results, and guidelines are provided for choosing the best crossvalidation procedure according to the particular features of the problem in hand.
Book ChapterDOI
A tutorial on learning with Bayesian networks
TL;DR: In this article, the authors discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models, including techniques for learning with incomplete data.
Journal ArticleDOI
An Algorithm for Fast Recovery of Sparse Causal Graphs
Peter Spirtes,Clark Glymour +1 more
TL;DR: An asymptotically correct algorithm whose complexity for fixed graph connectivity increases polynomially in the number of vertices, and may in practice recover sparse graphs with several hundred variables.
Journal ArticleDOI
Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network
TL;DR: This paper introduces the application of probability adapting in dynamic safety analysis rather than probability updating, and illustrates how Bayesian network (BN) helps to overcome limitations in BT.
Book ChapterDOI
Counting unlabeled acyclic digraphs
TL;DR: In this paper, a new method for enumerating unlabeled acyclic digraphs is developed, which involves computing the sum of the cyclic indices of the automorphism groups of the acyCLic diggraphs.
Related Papers (5)
Interestingness filtering engine: Mining Bayesian networks for interesting patterns
Rana Malhas,Zaher Al Aghbari +1 more