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- MakaleYalnız künye2024Scopus SJRQ1A comprehensive risk assessment framework for mooring risks at hydrocarbon berths using fuzzy rule-based Bayesian network and multi-attribute decision-making
Demirel, Hakan; Başhan, Veysi; Yucesan, Melih; Gul, Muhammet
Mooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study’s findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths.
AtıfScopus11WoS11OpenAlex13
İncele - BildiriYalnız künye2024An Analysis of Dependence Relationships Between Covid-19 Pandemic and Environmental Conditions Cases Study in Indonesia, England, and Türkiye
Handhayani, Teny; Caglar, Durdane Kocacoban; Wibowo, F.W.
This study explores the causal relationships between environmental conditions and Coronavirus disease 2019 (COVID-19) using Bayesian networks in three geographically distinct cities: Jakarta (Indonesia), London (England), and Istanbul (Türkiye). The datasets collected from these cities span from January 2020 to October 2022, encompassing a variety of environmental variables (Particulate Matter (PM2.5), temperature, humidity, precipitation, wind speed, and Ultraviolet (UV) index) and COVID-19 variables (new cases, deaths, and population density). By using the PC algorithm and Greedy Equivalence Search (GES) to generate causal graphs, and applying the Intersection-Validation (InterVal) method to derive agreement graphs, this research reveals significant geographical differences in how environmental factors influence COVID-19 dynamics. The Peter-Clark (PC) algorithm identifies conditional dependencies, while GES optimizes the structure of the causal graph. The study highlights the significant impact of temperature, UV index, and humidity on COVID-19 dynamics, emphasizing the importance of localized public health strategies tailored to specific environmental conditions. These findings contribute to the understanding of environmental influences on COVID-19 and offer valuable insights for future research and policy-making aimed at mitigating the impact of pandemics in diverse geographical settings.
AtıfScopus3OpenAlex2İncele - MakaleYalnız künye2024Scopus SJRQ1A stratified Bayesian decision-making model for occupational risk assessment of production facilities
Gul, Muhammet; Yucesan, Melih; Karci, Coskun
In the production industry, harmony and good management of the workplace environment, production machinery/vehicles, and workers are necessary to carry out production by occupational health and safety (OHS) principles. Therefore, occupational risk assessment (ORA) is crucial for manufacturing-based industries. When deciding on the prioritization of risks in ORA, adding to the analysis “how the parameters defining the risk changes in possible different states in the future” positively affects the soundness of decision-making. Therefore, this study aims to develop a unique ORA model handling future changes in the importance levels of risk parameters in the risk assessment process. To this aim, the concept of stratification and the best-worst method (BWM) are used together to determine the importance weights of the risk parameters in the ORA. In addition, the Bayesian version of BWM considers more than one expert's evaluations without losing information. In a nutshell, an approach called stratified Bayesian BWM (SBBWM) that can be used for further studies has been introduced to the literature. The technique determines the priority scores of each hazard by technique for order preference by similarity to the ideal solution sorting (TOPSIS-Sort) method. Thus, while determining each hazard's priority score and order, the class of this risk has also been determined. The proposed approach evaluated thirty-six risks encountered in manufacturing, storage, handling, and laboratory processes of a flour production facility. Control measures to be taken for each risk were also determined. Methodologically, various scenario analyses and sensitivity studies were conducted to reveal how the results changed in different conditions. The proposed approach provides a more comprehensive procedure for production facilities than traditional methods and avoids the deficiencies of traditional methods.
AtıfScopus18WoS16OpenAlex18İncele - MakaleAçık Erişim2023Scopus SJRQ3Investigation of issues affecting thermal comfort in water system underfloor heating applications of buildings with Bayesian networks
Erkol, Samet; Yücesan, Melih; Gül, Muhammet; Güneri, Ali Fuat
Thermal comfort is related to the stability of the ambient temperature. Constant changes in ambient temperature appear as a situation that negatively affects comfort. The selected building systems must be arranged to maintain this stability for the continuity of thermal comfort. In this study, issues affecting thermal comfort in water system underfloor heating applications of buildings are handled and analyzed using the Bayesian Network modeling methodology. Visual examples of the problems encountered in field applications are also given. Three different scenarios are tested with the constructed Bayesian Network model. In the first scenario, assumed that mechanical project failures were prevented. In this case, it was observed that the failure rate decreased by about 5%. In the second scenario, assumed that mechanical application failures are prevented along with mechanical project failures. The failure rate decreased by 11% compared to the first situation. The third scenario assumed that the mechanical project preparation phase was concluded without any problems, the mechanical project was implemented without any failures, and the mechanical system was commissioned without any problems. In the last scenario, the failure rate decreased by 14% compared to the first case, and the probability of not providing thermal comfort remained at 2%. As a result of these three scenarios, the possibility of not providing thermal comfort in the underfloor heating system is detailed and interpreted.
- MakaleYalnız künye2023Scopus SJRQ1A holistic failure modes and effects analysis for university plastic injection laboratory under Bayesian Network
Yucesan, Melih; Gul, Muhammet; Pamučar, Dragan
University Research & Development (R&D) laboratories are facilities used for research and manufacturing activities in engineering and basic science fields. Various filling materials are used in plastic R&D laboratories to improve the mechanical properties of thermoplastics or to recycle waste materials. The use of filling materials causes changes in plastic production parameters. This study dealt with the risks arising from extrusion, injection and shredder in a plastic injection laboratory, which led to the inability to manufacture the appropriate product under a holistic methodology. The proposed holistic methodology merges the concepts of failure mode and effects analysis (FMEA), fuzzy best-worst method (FBWM), and a rule-based three-hierarchical Bayesian network (RB-THBN). First, 15 root risks have been identified under a three-stage hierarchy with the support of laboratory decision makers. Then, questionnaires were created using the FMEA concept. The importance values (degree of belief-DoB) of three different FMEA parameters (severity, occurrence and detection) by experts for each risk have been calculated with FBWM. And finally, the RB-THBN has been created, and the final risk values have been computed using the linear utility function for the network. The system has been analyzed via a Bayesian modeling software. A validity test and a control measures planning has also been executed. According to the results, the highest root risk has been determined as “E2-The prepared mixture is not homogeneous” with a crisp risk score of 0.639559. In addition, “Failure due to extrusion” has been determined as the highest risk category with a score of 0.564059. It was discussed in detail that these risks arise from homogenization and how the used mixture should be homogenized. A comparative study among traditional FMEA, FMEA extended under FBWM and the proposed approach is performed to observe the differences in final ranking of root risks and to highlight the arguments that strengthen the advantages of the proposed method over the other two.
AtıfScopus7WoS7OpenAlex8İncele - MakaleAçık Erişim2021Scopus SJRQ3A BAYESIAN NETWORK-BASED APPROACH FOR FAILURE ANALYSIS IN WEAPON INDUSTRY
Yucesan, Melih; Gul, Muhammet; Guneri, Ali Fuat
Gun and rifle manufacturing contain various failures in the process of CNC machining, material supply, research & development, infrastructure and, operator. Due to these failures, the enterprise is exposed to great economic losses and a decrease in competition in the global market. In addition, failures in production cause events that seriously threaten human health. Failure analysis can increase safety by determining the cause of potential errors and taking measures for identified errors in the life cycle of the products. Therefore, this study employs a Bayesian Network (BN)-based modeling approach for capturing dependency among the basic events and obtaining top event probability. Firstly, a fault tree analysis (FTA) diagram is constructed, since its target is to pinpoint how basic event failures result in a top event (system) failure by an AND/OR logical gate. While, AND logical gate should take place in both cases, it is sufficient to realize one of the states in the OR logical gate. Then, a BN-based on fault tree transformation is applied. A case study in a leading weapon factory that produces various types of guns and rifles in the Black Sea region of Turkey is performed. For the application viewpoint, appropriate control measures can be taken into account to decrease the number of failed products based on the performed failure analysis.
AtıfScopus6WoS5OpenAlex7İncele - MakaleAçık Erişim2021A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method
Yucesan, Melih; Gul, Muhammet; Celik, Erkan
Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.
AtıfScopus105WoS93İncele - MakaleYalnız künye2020Scopus SJRQ1A manufacturing failure mode and effect analysis based on fuzzy and probabilistic risk analysis
Gul, Muhammet; Yucesan, Melih; Celik, Erkan
This study proposes an improved failure mode and effect analysis (FMEA) with fuzzy Bayesian Network (FBN) and fuzzy best-worst method (FBWM) to assess failures in plastic production. To eliminate the drawbacks of the classical risk priority number (RPN) computation of FMEA, this approach is developed. Unlike classical RPN, a new parameter hierarchy is constructed, and three sub-parameters are injected into the approach. These parameters are weighted by the aid of FBWM. Hereafter, a fuzzy rule-based system is constructed by incorporating Bayesian Network (BN). Also, a sensitivity analysis is performed to observe the final FMEA score changes in accordance with the change of subjective probability values. Finally, a comparative analysis with two approaches of classical FMEA and FBWM-based FMEA (without a fuzzy rule-based system incorporating BN) is fulfilled. The results of the study are strengthened with the experts’ opinions regarding the importance of failure modes for the final product and the whole system and supported them by experience feedback in the observed facility.
AtıfScopus68WoS59OpenAlex68İncele
