Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree

Chen, Zhe; Wu, Xiaona; Qin, Jian

doi:10.1016/j.jlp.2014.10.004

Cited by 27 publications

(4 citation statements)

References 37 publications

(28 reference statements)

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“…FMEA can reduce or eliminate the likelihood of failures and is used in various industries, such as the automotive, construction, aerospace, electrical, computer, safety and maintenance industries. Some benefits of FMEA fuzzy risk assessment in assessing the effectiveness of corrective actions can be applied in industry [26], health [27] and information systems [28]. The calculation of the FMEA fuzzy risk priority number (RPN) is more accurate and objective than the conventional FMEA RPN calculation method [29].…”

Section: Fuzzy Fmeamentioning

confidence: 99%

Development and Simulation of Cyberdisaster Situation Awareness Models

2022

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Cyberdisasters require an organization’s disaster team to be prepared. Disaster events are difficult to predict, but the impact of this risk on an organization is large. However, organizations sometimes struggle in being prepared for disaster situations. Here, awareness of disaster situations when analysing priority disasters (e.g., earthquakes and pandemics) and how to mitigate them can help an organization’s preparedness. Mitigation scenarios need to be determined and simulated so that a disaster team is ready to face disaster. Using Endsley’s situational awareness model and a tabletop exercise, this study aimed to help a disaster team determine cyberdisaster risk priority and assess a team’s preparedness for dealing with a cyberdisaster. The situation awareness model was divided into two stages: awareness of cyberdisaster situations and tabletop evaluations. Awareness of a disaster situation was carried out by determining the highest priority for disaster risk using the fuzzy failure modes and effects analysis (FMEA) method. The results of the first study show that the high-risk category contains ransomware attacks during pandemics and earthquakes. The second study performed a tabletop simulation questionnaire survey of earthquakes and ransomware attacks during a pandemic for several disaster teams with 152 respondents. The results of the survey evaluation of the earthquakes and ransomware attacks simulation survey show that the effect factors of cyberdisaster simulation decisions are 95% system capability (p < 0.05), 90% knowledge (p < 0.05), and 90% awareness of a disaster situation (p < 0.05); these factors show the effect of a disaster team’s decision during a tabletop simulation. The novelty of this research lies in building a model for how an organizational process determines the priority of a cyberdisaster tabletop simulation and the factors that contribute to increasing a disaster team’s awareness in dealing with cyberattacks.

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Section: Fuzzy Fmeamentioning

confidence: 99%

Development and Simulation of Cyberdisaster Situation Awareness Models

2022

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“…Moreover, the work of [9] proposed a new risk management approach known as the fuzzy rule base interface system in order to mitigate the failures. In the same way, the authors of [10][11][12][13][14][15][16] used the FMEA in order to identify and evaluate possible modes of failure, their causes and their effects. The defined the FMEA as a systematic analysis tool for product design.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Linguistic Conditional System as an Attention Mechanism in a Failure Mode and Effect Analysis

Baeza-Serrato

2024

Applied Sciences

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Fuzzy Inference System behavior can be described qualitatively using a natural language, which is known as the expert-driven approach to handling non-statistical uncertainty. Generally, practical applications involve conceptualizing the problem by integrating linguistic uncertainty and using data by integrating stochastic uncertainty. The proposed probabilistic fuzzy system uses the Gaussian Density Function (GDF) to assign a probability to input variables integrating stochastic uncertainty. In addition, a linguistic interpretation is used to project various categories of the GDF integrating linguistic uncertainty. Likewise, one of the relevant aspects of the proposal is to weigh each input variable according to the heuristic interpretation that determines the probability assigned to each of them a priori. Therefore, the main contribution of the research focuses on using the Bayesian Linguistic Conditional System (BLCS) as a mechanism of attention to relate the categories of the different input variables and find their posterior-weighted probability at a normalization stage. Finally, the knowledge base is established through linguistic rules, and the system’s output is a Bayesian classifier multiplying its normalized posterior conditional probabilities. The highest probability value of the knowledge base is identified, and the Risk Priority Number Weighted (RPNW) is determined using their respective posterior-normalized probabilities for each input variable. The results are expressed on a simple and precise scale from 1 to 10. They are compared with the Risk Priority Number (RPN), which results in a Failure Mode and Effect Analysis (FMEA). They show similar behaviors for multiple combinations in the evaluations while highlighting different scales.

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“…Especially, in recent years the linguistic variables and fuzzy logic have been applied in FMEA method. For example Carpitella et al (2018) and Kutlu and Ekmekçioğlu (2012) used fuzzy TOPSIS based fuzzy AHP, Fattahi and Khalilzadeh (2018) applied the extended MULTIMOORA and AHP under fuzzy environment to the risk evaluation, Kumru and Kumru (2013) applied fuzzy FMEA to improve purchasing process in a public hospital, Mandal and Maiti (2014) proposed fuzzy similarity value and possibility theory-based approach for risk analysis, Chen et al (2014) used a structural model based on the FMEA and fuzzy fault tree for a risk assessment of an oxygen-enhanced combustor, Chanamool and Naenna (2016) applied fuzzy FMEA to improve decision making process in an emergency department in hospitals, Dağsuyu et al (2016) used classical and fuzzy FMEA in a sterilization unit's risk analysis, Tooranloo and Ayatollah (2016) proposed a model for FMEA based on intuitionistic fuzzy approach and Adar et al (2017) analyzed risk by FMEA and Fuzzy FMEA of supercritical water gasification system used in the sewage sludge treatment [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An integrated approach by fmea & fuzzy prioritization method at pharmaceutical ındustry quality control

Yücenur

Çataltepe²,

Sakin³

2020

Cumhuriyet Science Journal

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Protecting public health, taking preventive measures and ensuring recovery in case of any disease are conditions for creating a healthy life. In order to create this life, the manufacturing process of the pharmaceutical industry needs to be formed carefully. In this study there is a risk analysis application into operational processes of a pharmaceutical company. In application study, operational processes of sample company were examined and analyzed from October to May. Within the scope of the study, a two-stage approach was proposed in the analysis of the data obtained. In the first phase, fishbone analysis was carried out to determine the risks in the operational processes and the potential risks in two separate production lines were determined. In the second stage, the risk prioritization method was used and risk priority numbers (RPN) were calculated for all risks. In all these analysis, more realistic and valid results were obtained with the usage of fuzzy logic and the calculations of RPNs were made more objective and independent from analysts. After the determination of RPNs, precautions were suggested for high risky failures. Following the implementation of precautions, new RPNs were calculated for all failures. The old and the new RPNs were compared for all risky failures and all precautions were examined with their impacts on the process. As a result, all examined failures' risk prioritization numbers were reduced in the ratio between 72% and 90%, the operational processes were improved.

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Risk assessment of an oxygen-enhanced combustor using a structural model based on the FMEA and fuzzy fault tree

Cited by 27 publications

References 37 publications

Development and Simulation of Cyberdisaster Situation Awareness Models

Development and Simulation of Cyberdisaster Situation Awareness Models

Bayesian Linguistic Conditional System as an Attention Mechanism in a Failure Mode and Effect Analysis

An integrated approach by fmea & fuzzy prioritization method at pharmaceutical ındustry quality control

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