Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

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Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network

Applied Sciences ◽

10.3390/app11083619 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3619

Author(s):

Weiliang Qiao ◽

Xiaoxue Ma ◽

Yang Liu ◽

He Lan

Keyword(s):

Bayesian Network ◽

Absorptive Capacity ◽

Fuzzy Theory ◽

Back Propagation ◽

Expert Elicitation ◽

Safety Level ◽

Prior Probabilities ◽

Safety Issues ◽

Northern Sea Route ◽

Probability Table

The safety level of the northern sea route (NSR) is a common concern for the related stakeholders. To address the risks triggered by disruptions initiating from the harsh environment and human factors, a comprehensive framework is proposed based on the perspective of resilience. Notably, the resilience of NSR is decomposed into three capacities, namely, the absorptive capacity, adaptive capacity, and restorative capacity. Moreover, the disruptions to the resilience are identified. Then, a Bayesian network (BN) model is established to quantify resilience, and the prior probabilities of parent nodes and conditional probability table for the network are obtained by fuzzy theory and expert elicitation. Finally, the developed Bayesian networkBN model is simulated to analyze the resilience level of the NSR by back propagation, sensitivity analysis, and information entropy analysis. The general interpretation of these analyses indicates that the emergency response, ice-breaking capacity, and rescue and anti-pollution facilities are the critical factors that contribute to the resilience of the NSR. Good knowledge of the absorptive capacity is the most effective way to reduce the uncertainty of NSR resilience. The present study provides a resilience perspective to understand the safety issues associated with the NSR, which can be seen as the main innovation of this work.

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Reliability Analysis of Metro Door System Based on Fuzzy Multi-State Bayesian Network

Journal of Engineering and Technological Sciences ◽

10.5614/j.eng.technol.sci.2021.53.5.9 ◽

2021 ◽

Vol 53 (5) ◽

pp. 210509

Author(s):

Zhenliang Fu ◽

Na Li ◽

Xueyan Tian ◽

Yonghua Li ◽

Ziqiang Sheng

Keyword(s):

Missing Data ◽

Bayesian Network ◽

Reliability Analysis ◽

Fuzzy Theory ◽

Probability Model ◽

Fault Tree ◽

Fault Tree Analysis ◽

Practical Experience ◽

Prior Probabilities ◽

System A

Considering the shortcomings of the fault tree analysis (FTA) method in the reliability analysis of metro door systems, Bayesian network (BN) and fuzzy theory were introduced to establish the failure probability model of a metro door system. A fault tree of the metro door system was established based on the structure of the metro door, the operation data record and the practical experience of relevant engineers. The BN of the metro door system was constructed based on the fault tree. For the problem that the prior probabilities of root nodes with missing data were unavailable, fuzzy theory was introduced to convert the expert language values on these missing data nodes to corresponding prior probabilities, which were substituted into the BN along with the root nodes whose prior probabilities were obtained from the operation fault data to calculate the leaf node probability. Cause analysis of the metro door system was performed with bi-directional reasoning of BN, which provided a way to find the key factors that caused door faults and the metro door system fault probabilities.

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Human Factor Analysis Model of Civil Aviation Incidents Based on Bayesian Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3074 ◽

2013 ◽

Vol 756-759 ◽

pp. 3074-3078 ◽

Cited By ~ 1

Author(s):

Ming Ming Zhang ◽

Yan Yang Wang ◽

Min Luo ◽

Wen Zhong Tang

Keyword(s):

Factor Analysis ◽

Human Factors ◽

Bayesian Network ◽

Human Factor ◽

Score Function ◽

Hill Climbing ◽

Civil Aviation ◽

Analysis Model ◽

Factor Analysis Model ◽

Specific Impact

To generate a model which can provides detailed data analysis supportive in aviation incident analysis, a human factor analysis model based on Bayesian network theory is established. This model is a Bayesian network which uses three layers nodes to represent causality between human factors and incidents. The specific impact degree of human factors on aviation incidents is represented by conditional probability parameters of the model. The model structure, constructed by combing hill-climbing search method with CH score function, coincides with the actual data. This model is useful in aviation incident analyses and deductions.

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HFACS Analysis of U.S. General Aviation Accidents Using Bayesian Network

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641403 ◽

2020 ◽

Vol 64 (1) ◽

pp. 1655-1659

Author(s):

Chuyang Yang ◽

John H. Mott

Keyword(s):

Human Factors ◽

Bayesian Network ◽

General Aviation ◽

Transportation Safety ◽

Aviation Industry ◽

Contributing Factors ◽

The Sustainable Development ◽

Aviation Accidents ◽

Prior Probabilities ◽

Probability Table

Safety is one of the most important factors that affects the sustainable development of the aviation industry. With the increasing robustness of technologies, humans have played a progressively more important causal role in aviation accidents. This paper applies an HFACS-BN model (HFACS: Human Factors Analysis and Classification System; BN: Bayesian Network) to analyze the root causes of aviation accidents. General aviation (GA) accident reports were collected from the U.S. National Transportation Safety Board (NTSB) accident database. The authors encoded the human factors of sample cases based on the HFACS framework and constructed a corresponding BN. From this work, parameter estimation associated with a conditional probability table (CPT) was conducted to determine prior probabilities of contributing factors, and a sensitivity test was conducted to determine the most significant factors. This study provides guidance to the federal government to facilitate risk management in order to reduce fatal general aviation accidents.

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Equipment Impact Rate Assessment Using Bayesian Networks

Volume 2: Pipeline Safety Management Systems; Project Management, Design, Construction and Environmental Issues; Strain Based Design; Risk and Reliability; Northern Offshore and Production Pipelines ◽

10.1115/ipc2016-64381 ◽

2016 ◽

Author(s):

Smitha D. Koduru ◽

Dongliang Lu

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Common Ground ◽

Fault Tree ◽

Prior Probabilities ◽

Tree Methods ◽

Reporting Tool ◽

Triggering Events ◽

Intuitive Method ◽

Tree Method

Bayesian networks offer an intuitive method of modelling causal relationships between the triggering events that lead to equipment impact on a pipeline. This method offers an advantage over the more well-known fault-tree methods due to its ability to use Bayesian inference for updating the prior probabilities of triggering events that lead to equipment impact such as, failure of permanent markers, use of one-call system, or failure of right-of-way patrol. In this paper, a modelling approach for a Bayesian network for equipment impact assessment, based on the available fault-tree method, is demonstrated. The advantages of the Bayesian network, such as updating the occurrence rates of basic triggering events and tracking information flow based on partial and incomplete information are illustrated by using the event data available from the damage incident reporting tool (DIRT) of Common Ground Alliance (CGA).

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An Application of the HFACS Method to Aviation Accidents in Africa

Aviation Psychology and Applied Human Factors ◽

10.1027/2192-0923/a000093 ◽

2016 ◽

Vol 6 (1) ◽

pp. 33-38 ◽

Cited By ~ 4

Author(s):

Isaac Munene

Keyword(s):

Human Factors ◽

Physical Environment ◽

Human Factor ◽

Flight Training ◽

African Countries ◽

Causal Factors ◽

Aviation Accidents ◽

Factors Analysis ◽

Adverse Weather ◽

Decision Errors

Abstract. The Human Factors Analysis and Classification System (HFACS) methodology was applied to accident reports from three African countries: Kenya, Nigeria, and South Africa. In all, 55 of 72 finalized reports for accidents occurring between 2000 and 2014 were analyzed. In most of the accidents, one or more human factors contributed to the accident. Skill-based errors (56.4%), the physical environment (36.4%), and violations (20%) were the most common causal factors in the accidents. Decision errors comprised 18.2%, while perceptual errors and crew resource management accounted for 10.9%. The results were consistent with previous industry observations: Over 70% of aviation accidents have human factor causes. Adverse weather was seen to be a common secondary casual factor. Changes in flight training and risk management methods may alleviate the high number of accidents in Africa.

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A Dynamic Fault Tree Analysis Method Based on Discrete-time Bayesian Network for the Core Processing System of IMA

2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS) ◽

10.1109/icsess49938.2020.9237725 ◽

2020 ◽

Author(s):

Qin Zhang ◽

Lisong Wang ◽

Hao Rong ◽

Qingfan Gu ◽

Yang Hong ◽

...

Keyword(s):

Bayesian Network ◽

Discrete Time ◽

Fault Tree ◽

Fault Tree Analysis ◽

Processing System ◽

Analysis Method ◽

Tree Analysis ◽

The Core ◽

Dynamic Fault Tree

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On the Influence of Human Factors on Safety of Remotely-Controlled Merchant Vessels

Applied Sciences ◽

10.3390/app11031145 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1145

Author(s):

Krzysztof Wróbel ◽

Mateusz Gil ◽

Chong-Ju Chae

Keyword(s):

Human Factors ◽

Human Factor ◽

Potential Influence ◽

General Belief ◽

Causal Factors ◽

Physiological Conditions ◽

Factors Analysis ◽

Important Barrier ◽

Human Operators ◽

Expert Study

With numerous efforts undertaken by both industry and academia to develop and implement autonomous merchant vessels, their safety remains an utmost priority. One of the modes of their operation which is expected to be used is a remote control. Therein, some, if not all, decisions will be made remotely by human operators and executed locally by a vessel control system. This arrangement incorporates a possibility of a human factor occurrence. To this end, a variety of factors are known in the literature along with a complex network of mutual relationships between them. In order to study their potential influence on the safety of remotely-controlled merchant vessels, an expert study has been conducted using the Human Factors Analysis and Classification System-Maritime Accidents (HFACS–MA) framework. The results indicate that the most relevant for the safety of this prospective system is to ensure that known problems are properly and timely rectified and that remote operators maintain their psycho- and physiological conditions. The experts elicited have also assigned higher significance to the causal factors of active failures than latent failures, thus indicating a general belief that operators’ actions represent the final and the most important barrier against accident occurrence.

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Application of Bayesian Network in Safety Evaluation of Metro Construction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.838-841.1463 ◽

2013 ◽

Vol 838-841 ◽

pp. 1463-1468

Author(s):

Xiang Ke Liu ◽

Zhi Shen Wang ◽

Hai Liang Wang ◽

Jun Tao Wang

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Posterior Probability ◽

Fault Tree ◽

Fault Tree Analysis ◽

Safety Evaluation ◽

Tree Analysis ◽

Tunnel Blasting ◽

Better Than

The paper introduced the Bayesian networks briefly and discussed the algorithm of transforming fault tree into Bayesian networks at first, then regarded the structures impaired caused by tunnel blasting construction as a example, introduced the built and calculated method of the Bayesian networks by matlab. Then assumed the probabilities of essential events, calculated the probability of top event and the posterior probability of each essential events by the Bayesian networks. After that the paper contrast the characteristics of fault tree analysis and the Bayesian networks, Identified that the Bayesian networks is better than fault tree analysis in safety evaluation in some case, and provided a valid way to assess risk in metro construction.

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