Mine safety assessment based on basic event importance: grey relational analysis and bow tie model

Xu, Qingwei; Xu, Kaili; Li, Li; Yao, Xiwen

doi:10.1098/rsos.180397

Cited by 16 publications

(24 citation statements)

References 35 publications

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“…Since 1949, 126 extraordinarily severe gas explosion accidents (defined as killing 30 or more people in one accident) occurred, and 7,502 people died (State Administration of Work Safety [SAWS], ; Wang & Li, ). Those accidents not only caused significant loss of life and property damage but also had a negative influence on society (Xu, Xu, Li, & Yao, ).…”

Section: Introductionmentioning

confidence: 99%

Causation Analysis of Risk Coupling of Gas Explosion Accident in Chinese Underground Coal Mines

Zhang

You

et al. 2019

Risk Analysis

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The coal mine production industry is a complex sociotechnical system with interactive relationships among several risk factors. Currently, causation analysis of gas explosion accidents is mainly focused on the aspects of human error and equipment fault, while neglecting the interactive relationships among risk factors. A new method is proposed through risk coupling. First, the meaning of risk coupling of a gas explosion is defined, and types of risk coupling are classified. Next, the coupled relationship and coupled effects among risk factors are explored through combining the interpretative structural modeling (ISM) and the NK model. Twenty-eight representative risk factors and 16 coupled types of risk factors are obtained through analysis of 332 gas explosion accidents in coal mines in China. Through the application of the combined ISM-NK model, an eight-level hierarchical model of risk coupling of a gas explosion accident is established, and the coupled degrees of different types of risk coupling are assessed. The hierarchical model reveals that two of the 28 risk factors, such as state policies, laws, and regulations, are the root risk factors for gas explosions; nine of the 28 risk factors, such as flame from blasting, electric spark, and local gas accumulation, are direct causes of gas explosions; whereas 17 of the risk factors, such as three-violation actions, ventilation system, and safety management, are indirect ones. A quantitative analysis of the NK model shows that the probability of gas explosion increases with the increasing number of risk factors. Compared with subjective risk factors, objective risk factors have a higher probability of causing gas explosion because of risk coupling.

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Section: Introductionmentioning

confidence: 99%

Causation Analysis of Risk Coupling of Gas Explosion Accident in Chinese Underground Coal Mines

Zhang

You

et al. 2019

Risk Analysis

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“…The cloud model [22][23][24] was introduced to take randomness into consideration, the integrated weight method [33] was adopted, and a new algorithm was proposed to determine the preference coefficient of the integrated weight based on the least square method [35] in this study. To validate the cloud model and integrated weight algorithm for the safety assessment, grey relational analysis [16][17][18] was applied. Then, the causes and sub-causes of dangerous and harmful factors were identified by cause and effect analysis [36,37], and the causes mainly including dust, noise, toxic gas, mechanical injury, empyrosis and electric shock.…”

Section: Framework Of the Proposed Safety Assessment Methodsmentioning

confidence: 99%

“…Grey relational analysis is widely used in grey system theory [16][17][18], which is applied to calculate the grey relational degree among different evaluated indicators, and the safety level of the casting workshop can be achieved by grey relational degree. The detailed process of grey relational analysis is as follows.…”

Section: Grey Relational Analysismentioning

confidence: 99%

“…Frequently used safety assessment methods include fuzzy evaluation [13][14][15], grey relational analysis [16][17][18], neural network [19][20][21], and the cloud model [22][23][24]. To better evaluate various failure modes, a new fuzzy hybrid model to analyze failure modes and effects was proposed by Fattahi and Khalilzadeh [13], in which a fuzzy weight risk priority was considered for each failure.…”

Section: Introductionmentioning

confidence: 99%

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Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect–LOPA to Protect Employee Health

Fang

2020

IJERPH

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Safety assessment of a casting workshop will provide a clearer understanding of the important safety level required for a foundry. The main purpose of this study was to construct a composite safety assessment method to protect employee health using the cloud model and cause and effect–Layer of Protection Analysis (LOPA). In this study, the weights of evaluation indicators were determined using the subjective analytic hierarchy process and objective entropy weight method respectively. Then, to obtain the preference coefficient of the integrated weight more precisely, a new algorithm was proposed based on the least square method. Next, the safety level of the casting workshop was presented based on the qualitative and quantitative analysis of the cloud model, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account; the validity of cloud model evaluation was validated by grey relational analysis. In addition, cause and effect was used to proactively identify factors that may lead to accidents. LOPA was used to correlate corresponding safety measures to the identified risk factors. 6 causes and 19 sub-causes that may contribute to accidents were identified, and 18 potential remedies, or independent protection layers (IPLs), were described as ways to protect employee health in foundry operations. A mechanical manufacturing business in Hunan, China was considered as a case study to demonstrate the applicability and benefits of the proposed safety assessment approach.

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“…To validate the cloud model adopted in the evaluation process of the red wine sample, this section compares the evaluation results of AE-C using gray relational analysis. A brief procedure of gray relational analysis is presented next (Aslantas, Ekici, & Çiçek, 2018;Xu & Xu, 2018c;Xu, Xu, Li, & Yao, 2018a).…”

Section: Gray Relational Analysis Of Ae-cmentioning

confidence: 99%

Quality evaluation of Chinese red wine based on cloud model

2019

J Food Biochem

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Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. Practical applications In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.

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Mine safety assessment based on basic event importance: grey relational analysis and bow tie model

Cited by 16 publications

References 35 publications

Causation Analysis of Risk Coupling of Gas Explosion Accident in Chinese Underground Coal Mines

Causation Analysis of Risk Coupling of Gas Explosion Accident in Chinese Underground Coal Mines

Safety Assessment of Casting Workshop by Cloud Model and Cause and Effect–LOPA to Protect Employee Health

Quality evaluation of Chinese red wine based on cloud model

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