Risk Assessment in Underground Coalmines Using Fuzzy Logic in the Presence of Uncertainty

Tripathy, Debi Prasad; Kumar, Charan

doi:10.1007/s40033-018-0154-7

Cited by 17 publications

(21 citation statements)

References 17 publications

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“…The proper definition of linguistic variables is based on expert knowledge and depends on the industry type. The examples of the definition of linguistic variables for risk in the mining industry are given, e.g., in [ 48 , 49 ].…”

Section: Fuzzy-based Risk Analysis Methodsmentioning

confidence: 99%

“…The main components of the Mamdani fuzzy model are fuzzification, knowledge base, fuzzy interference system, and defuzzification [ 48 ]. Figure 4 represents the graphical view of the implemented Mamdani fuzzy model in the defined research area.…”

Section: Fuzzy-based Risk Analysis Methodsmentioning

confidence: 99%

“…The underground coal mines’ safety risk is analyzed as well, e.g., in [ 48 ]. The authors use the fuzzy reasoning approach and Mamdani fuzzy logic model developed in MATLAB’s fuzzy logic toolbox.…”

Section: Fuzzy Theory In Mining Safety Problemsmentioning

confidence: 99%

See 2 more Smart Citations

Cyber-Attacks Risk Analysis Method for Different Levels of Automation of Mining Processes in Mines Based on Fuzzy Theory Use

Tubis

Werbińska-Wojciechowska

Góralczyk

et al. 2020

Sensors

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The rising automation level and development of the Industry 4.0 concept in the mining sector increase the risk of cyber-attacks. As a result, this article focuses on developing a risk analysis method that integrates Kaplan’s and Garrick’s approach and fuzzy theory. The proposed approach takes into account the level of automation of the operating mining processes. Moreover, it follows five main steps, including identifying the automation level in a selected mine, definition of cyber-attack targets, identification of cyber-attack techniques, definition of cyber-attack consequences, and risk ratio assessment. The proposed risk assessment procedure was performed according to three cyber-attack targets (databases, internal networks, machinery) and seven selected types of cyber-attack techniques. The fuzzy theory is implemented in risk parameter estimation for cyber-attack scenario occurrence in the mining industry. To illustrate the given method’s applicability, seven scenarios for three levels of mine automation are analyzed. The proposed method may be used to reveal the current cybersecurity status of the mine. Moreover, it will be a valuable guide for mines in which automation is planned in the near future.

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Section: Fuzzy-based Risk Analysis Methodsmentioning

confidence: 99%

Section: Fuzzy-based Risk Analysis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Cyber-Attacks Risk Analysis Method for Different Levels of Automation of Mining Processes in Mines Based on Fuzzy Theory Use

Tubis

Werbińska-Wojciechowska

Góralczyk

et al. 2020

Sensors

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“…Li et al [29] applied the FL technique for a risk analysis of long WS pipelines. Tripathy et al [30] used an FL to analyze the risk of care in coal mines. Chen et al [31] developed a decision-making technique based on FL to select the supplier chain in the supply chain scheme.…”

Section: Related Workmentioning

confidence: 99%

A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

et al. 2019

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The standard manufacturing organizations follow certain rules. The highest ubiquitous organizing principles in infrastructure design are modular idea and symmetry, both of which are of the utmost importance. Symmetry is a substantial principle in the manufacturing industry. Symmetrical procedures act as the structural apparatus for manufacturing design. The rapid growth of population needs outstrip infrastructure such as roads, bridges, railway lines, commercial, residential buildings, etc. Numerous underground facilities are also installed to fulfill different requirements of the people. In these facilities one of the most important facility is water supply pipelines. Therefore, it is essential to regularly analyze the water supply pipelines’ risk index in order to escape from economic and human losses. In this paper, we proposed a simplified hierarchical fuzzy logic (SHFL) model to reduce the set of rules. To this end, we have considered four essential factors of water supply pipelines as input to the proposed SHFL model that are: leakage, depth, length and age. Different numbers of membership functions are defined for each factor according to its distribution. The proposed SHFL model takes only 95 rules as compared to the traditional mamdani fuzzy logic method that requires 1225 rules. It is very hard and time consuming for experts to design 1225 rules accurately and precisely. Further, we proposed a Do-it-Yourself (DIY) system for the proposed SHFL method. The purpose of the DIY system is that one can design the FIS model according to his or her need.

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“…The fuzzy concepts were used in the suggested methodology. Tripathy et al [19] suggested a technique to assess the safety risk index of coal mines. The proposed method was based on the fuzzy reasoning methodology and authors have used the fuzzy logic method despite the availability of other similar techniques.…”

Section: Introductionmentioning

confidence: 99%

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

et al. 2019

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As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.Processes 2019, 7, 182 2 of 15 different methods in order to assess water supply pipelines. An efficient risk assessment methodology is fundamental to take measures in time to escape from accidents.Recently, the fuzzy logic (FL) method has grasped the consideration of various scholars and has been widely used in several areas for different purposes [9,10]. Fuzzy logic methods have been extensively used for risk index analysis and assessment. Fuzzy inference system (FIS) can be used to solve the problems related to the exact mathematical models. However, conventional fuzzy inference systems are not suitable due to its rules-explosion with every new entry of variables. For a fuzzy model having q input parameters, for each input parameters p MFs are defined. Then, for a full fuzzy inference system q p fuzzy rules are required, such as in [11,12] a fuzzy inference system has been designed where there are 12 input variables and for each variable five MFs are allocated. Hence, the entire number of rules obligatory to completely implement the system are 5 12 . It is particularly difficult for an expert to incorporate that large number of rules with attention. Any abnormality in rule designing can cause casualties of people, wastage of money or both losses. Hence, the minimization of rules in rule-base is an issue of high concern. To overcome the issue of rule-explosion, a solution is to divide the fuzzy inference system in sub-modules in a hierarchical form. In this hierarchical fuzzy logic method, the low-level modules provide fractional solutions; these fractional solutions are then combined in the high-level modules to provide...

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Risk Assessment in Underground Coalmines Using Fuzzy Logic in the Presence of Uncertainty

Cited by 17 publications

References 17 publications

Cyber-Attacks Risk Analysis Method for Different Levels of Automation of Mining Processes in Mines Based on Fuzzy Theory Use

Cyber-Attacks Risk Analysis Method for Different Levels of Automation of Mining Processes in Mines Based on Fuzzy Theory Use

A Water Supply Pipeline Risk Analysis Methodology Based on DIY and Hierarchical Fuzzy Inference

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

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