Incorporating CREAM and MCS into fault tree analysis of LNG carrier spill accidents

Zhou, Tuqiang; Wu, Chaozhong; Zhang, Junyi; Zhang, Di

doi:10.1016/j.ssci.2017.03.015

Cited by 70 publications

(38 citation statements)

References 18 publications

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“…The FTA is a typical method and an accidental evolutionary logic analysis tool used to estimate the safety and reliability of a complex system. It was used to establish effectively and clearly relationships among accident risk factors using LNG ship accident analysis (Zhou et al, 2017), ship grounding in Arctic (Kum et al, 2015), explosion of crude oil tanks . Moreover, the FTA can reproduce the evolution of underlying factors and high-level accidents, which helps to understand the development course of collision accidents and provide guidance for the formulation of RCOs.…”

Section: Fault Tree Analysismentioning

confidence: 99%

Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters

et al. 2019

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With the global warming and a large amount of sea ice melting, the available Arctic Sea Route has greatly enhanced the value of Arctic shipping. Ship operations under icebreaker assistance have become an essential way to facilitate the safe navigation of merchant vessels sailing through the Arctic Sea Route in ice-covered waters, but they can also put the crew and the ship in danger caused by a possible collision between the assisted ship and the icebreaker. In this paper, a dedicated Human and Organizational Factors (HoFs) model of ship collision accidents between an assisted ship and an icebreaker is developed and analyzed with the aim to identify and classify collision risk factors. First, a modified model of the Human Factors Analysis and Classification System (HFACS) for collision accidents between a ship and an icebreaker in ice-covered waters is proposed, which helps to analyze ship collision reports. Then, a Fault Tree Analysis (FTA) model is utilized to analyze the fundamental collision risk factors according to the statistical analysis of accident reports and expert judgments based on the HFACS-SIBCI model. Finally, qualitative analysis is carried out to analyze collision risk factors under icebreaker assistance, where Risk Control Options (RCOs) are formulated. An important guidance for the risk control of ship collisions during icebreaker assistance in ice-covered waters is provided for lawmakers and shipping companies.

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Section: Fault Tree Analysismentioning

confidence: 99%

Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters

et al. 2019

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“…In the quantitative analysis of FAT, the critical importance coefficient is often used. This coefficient refers to the ratio of the relative change rate of the occurrence probability of a basic event to the change rate of the occurrence probability of the top event, also known as the "risk importance factor" [39], [40]. FAT analysis is used to quantitatively analyse the accident of the underground electric locomotive colliding with people, and its probability of a top event of the "AND gate" is as follows:…”

Section: B Demonstration Of the Reliability Of The Modelmentioning

confidence: 99%

An Underground Mine Risk Identification Model and Safety Management Method Based on Explanation Graph-Probabilistic Multi-Plan Analysis (EG-PMPA)

et al. 2020

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“…In [18], Bao et al proposed a hierarchical dynamic trust management protocol for clustered wireless sensor networks and develop a probability model using stochastic Petri net techniques to analyze the performance. Zhang et al [19] proposed a multi-level trust management framework. In this framework, three levels of trust, namely subjective trust, objective reputation, and recommendation trust, are used to establish trust relationships among nodes.…”

Section: Related Workmentioning

confidence: 99%

Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

Yin

2019

J Wireless Com Network

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Trust management is considered as an effective complementary mechanism to ensure the security of sensor networks. Based on historical behavior, the trust value can be evaluated and applied to estimate the reliability of the node. For the analysis of the possible attack behavior of malicious nodes, we proposed a trust evaluation model with entropy-based weight assignment for malicious node's detection in wireless sensor networks. To mitigate the malicious attacks such as packet dropping or packet modifications, multidimensional trust indicators are derived from communication between adjacent sensor nodes, and direct and indirect trust values will be estimated based on the corresponding behaviors of those sensor nodes. In order to improve the validity of trust quantification and ensure the objectivity of evaluation, the entropy weight method is applied to determine the proper value of the weight. Finally, the indirect trust value and direct trust value are synthesized to obtain the overall trust. Experimental results show that the proposed scheme performs well in terms of the identification of malicious node.

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Incorporating CREAM and MCS into fault tree analysis of LNG carrier spill accidents

Cited by 70 publications

References 18 publications

Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters

Use of HFACS and fault tree model for collision risk factors analysis of icebreaker assistance in ice-covered waters

An Underground Mine Risk Identification Model and Safety Management Method Based on Explanation Graph-Probabilistic Multi-Plan Analysis (EG-PMPA)

Trust evaluation model with entropy-based weight assignment for malicious node’s detection in wireless sensor networks

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