Use of Bayesian Network for Human Reliability Modelling: Possible Benefits and an Example of Application

2017 ◽  
pp. 189-223
Author(s):  
Maria Chiara Leva ◽  
Peter Friis Hansen
Keyword(s):  
Bayesian Network ◽  
Human Reliability ◽  
Reliability Modelling

scholarly journals Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

2018 ◽  
Vol 5 (4) ◽  
pp. 171438 ◽  
Author(s):  
Zhiqiang Li ◽  
Tingxue Xu ◽  
Junyuan Gu ◽  
Qi Dong ◽  
Linyu Fu
Keyword(s):  
Markov Process ◽  
Bayesian Network ◽  
Markov Models ◽  
Dynamic Bayesian Network ◽  
Control Unit ◽  
The State ◽  
Absorbing Set ◽  
Imperfect Repair ◽  
Dynamic Fault Tree ◽  
Reliability Modelling

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis

2014 ◽  
Vol 132 ◽  
pp. 1-8 ◽  
Author(s):  
Mashrura Musharraf ◽  
David Bradbury-Squires ◽  
Faisal Khan ◽  
Brian Veitch ◽  
Scott MacKinnon ◽  
...  
Keyword(s):  
Data Collection ◽  
Bayesian Network ◽  
Reliability Analysis ◽  
Experimental Technique ◽  
Network Approach ◽  
Human Reliability ◽  
Human Reliability Analysis

scholarly journals A fuzzy and Bayesian network CREAM model for human reliability analysis – The case of tanker shipping

2018 ◽  
Vol 105 ◽  
pp. 149-157 ◽  
Author(s):  
Qingji Zhou ◽  
Yiik Diew Wong ◽  
Hui Shan Loh ◽  
Kum Fai Yuen
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
Human Reliability ◽  
Human Reliability Analysis

BN-SLIM: A Bayesian Network methodology for human reliability assessment based on Success Likelihood Index Method (SLIM)

2020 ◽  
Vol 193 ◽  
pp. 106647 ◽  
Author(s):  
Shokoufeh Abrishami ◽  
Nima Khakzad ◽  
Seyed Mahmoud Hosseini ◽  
Pieter van Gelder
Keyword(s):  
Bayesian Network ◽  
Reliability Assessment ◽  
Human Reliability ◽  
Index Method

Multi-state reliability assessment for hydraulic lifting system based on the theory of dynamic Bayesian networks

2016 ◽  
Vol 230 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Chun Su ◽  
Ning Lin ◽  
Yequn Fu
Keyword(s):  
Bayesian Network ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Mechanical Systems ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Effect Analysis ◽  
Analysis And Design ◽  
Reliability Modelling ◽  
Lifting System

Mechanical systems and their components usually have multiple failure modes and different performance states. Most existing system reliability modelling theories are developed on the basis of binary logic, which lack sufficient ability to describe the above phenomena. In this article, dynamic Bayesian network theory is employed to evaluate the multi-state reliability of a hydraulic lifting system. First, failure mode and effect analysis and structural analysis and design technique are comprehensively applied to analyse the functionalities and failure modes of the components. Afterwards, the time factor is integrated into the model by considering the state transition of the components. In this way, the multi-state reliability model of the system is established by dynamic Bayesian network. The reliability assessment and diagnostic analysis are performed by taking advantage of the dynamic Bayesian network’s bi-directional reasoning ability, and the results are in good agreement with actual situation. It shows that the proposed approach is effective and convenient for multi-state reliability modelling and analysis for mechanical systems.

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