Fuzzy Failure Mode And Effect Analysis Research Articles

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Failure analysis of floating offshore wind turbines based on a fuzzy failure mode and effect analysis model

AbstractThe surging global demand for renewable energy has fueled the expansion of offshore wind energy, leveraging superior wind profiles and technological advancements in offshore wind turbine technology. Despite this growth, the operational experience of floating offshore wind turbines still needs to be improved as an emerging technology. Addressing this knowledge gap, our study compiles a comprehensive database of failure events associated with these turbines. Employing a rigorous analytical approach through the Fuzzy Failure Mode and Effect Analysis (FMEA) methodology, we conduct an in‐depth failure analysis of floating offshore wind turbines. This investigation allows us to identify the most critical failure modes and pinpoint components vulnerable to failures. The paper delves into the root causes of these significant failure modes, proposing preventive and corrective measures based on our findings. Our recommendations serve as a strategic guide for stakeholders, offering insights to enhance offshore wind turbines’ design, operation, and maintenance practices and the broader wind farm infrastructure. Despite this, further clarification on the application and highlights of the fuzzy FMEA model is warranted in the abstract, a point we address in the revised conclusion to enhance our study's overall clarity and completeness.

An integrated risk and productivity assessment model for public–private partnership projects using fuzzy inference system

Infrastructural projects based on public–private partnership (PPP) schemes usually struggle with large amounts of resources. Given such projects’ relatively long lifetime, it is expected to encounter a wide range of risks when executing them. This implies that increasing productivity based on cost, time, and quality goals requires accurately identifying and assessing risks in these projects. This study aims at risk assessment and analysing productivity in PPP projects based on an integrated analysis method. To this end, first, library studies are utilized to identify the most significant risks by surveying the PPP-based urban underground construction projects. Expert opinions and the fuzzy Delphi (FD) technique are then employed in three stages to identify 48 secondary risk factors. By identifying the sources of these secondary risk factors, we identify five primary risk dimensions: government, financing, operation, constructability, and organization-related risks. A questionnaire is distributed to acquire experts’ opinions and screen the most critical risks by integrating FD with fuzzy failure mode and effects analysis (FFMEA). Results showed that, in total, 23 secondary risk factors in the five primary risk dimensions exhibited risk priority number (RPNNormalized) values above 0.5, indicating their relatively more critical impacts. In the next phase, a multi-stage fuzzy inference system is designed to evaluate the impacts of critical risks on the productivity of PPP projects. Output results of the inference system indicated very high, high, and medium impacts of the risks on the overall productivity of the PPP projects in terms of time, cost, and quality, respectively.

Marine diesel engine turbocharger fouling phenomenon risk assessment application by using fuzzy FMEA method

Turbocharger fouling phenomenon was analyzed from the risk assessment perspective in this study. The research employed exhaust system and turbocharger equipment of commercial ship that equipped with a Doosan-MAN B&W 6S50 MC-C diesel engine was used as the main research materials, and utilized the fuzzy Failure Mode and Effects Analysis (FMEA) based on the expert system as a methodical approach. The experts revealed different types of turbocharger fouling failure modes (FMs), along with their respective causes and subsequent consequences. Following that, the specialists allocated an O, S, and D score to each FM. Within the framework of fuzzy logic, the process entails the establishment of input and output membership functions, as well as the construction of a fuzzy model incorporating an inference mechanism and a rule base. Based on the analysis findings, the three primary factors are as follows: low cylinder compression pressure with a Fuzzy Risk Priority Number (FRPN) score of 6.95, high main engine fuel oil consumption with a score of 6.92, and high CO, CO2, SOx emissions with a 6.45. The phenomenon of turbocharger fouling, being an inherent occurrence, has significant ramifications on the main engine, the vessel as a whole, and the ecological surroundings. The quantitative results presented in this study provide valuable insights into the risks associated with maritime endeavors. The data generated from this research can be used by stakeholders in the maritime industry to better understand this situation and take proactive measures to mitigate potential risks in the future. Furthermore, the findings of the research provide corroboration for the implementation of predictive maintenance procedures.

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

PurposeThis study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.Design/methodology/approachBased on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.FindingsThe results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.Practical implicationsThis study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.Originality/valueThis paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

A quantitative dynamic risk assessment for ship operation using the fuzzy FMEA: The case of ship berthing/unberthing operation

Ships are exposed to static risks that stem from the nature of the operation as well as human, machine, and environmental factors and have a complex and multi-layered system that also contains dynamic risks. For this reason, maritime studies to reduce and prevent risks in ship operations continue by safety researchers. The present study proposes a quantitative maritime safety analysis based on Fuzzy Failure Mode and Effect Analysis (FMEA) methodology to evaluate potential risks that may occur during ship operations. To demonstrate the practical application of the proposed approach, failure modes are determined in ship berthing/unberthing operations and prioritized according to risk values. According to the results, the failure modes which have higher risk rates are identified as Fatigue/Individual error, Very strong wind, Excessive heat, Low tide, Increase/decrease in ship speed. Finally, appropriate corrective or preventive actions are recommended to control failure modes that affect the safety of the ship and to reduce or eliminate the effects of these failures with a proactive approach. This paper theoretically contributes for marine safety inspectors, safety researchers, and HSEQ managers to identify potential hazards, effects, and consequences in case of berthing/unberthing operations.

Risk Analysis in the Food Cold Chain Using Decomposed Fuzzy Set-Based FMEA Approach

Risk analysis is employed across various domains, including the increasingly vital food supply chain, particularly highlighted by the COVID-19 pandemic. This study focuses on applying decomposed fuzzy sets (DFS), a novel extension of intuitionistic fuzzy sets, within the context of the food cold chain. The objective is to develop “Decomposed Fuzzy Set-Based FMEA (DF FMEA)” by extending the well-known failure mode and effect analysis (FMEA) method to DFS, to assess risks in the food cold chain. The functional and dysfunctional questions related to the severity, occurrence, and detectability of the identified risks; they were addressed to three experts working on the food cold chain. The purpose is to prevent an inconsistent assignment considering the uncertainty and indecision of decision makers. Due to the implementation of the DF FMEA, the identified risks were prioritized as follows: “Financial Risks” held the highest priority, followed by “Delivery Risks”, “Technological Ability Risks”, “Environmental Risks”, “Quality Risks”, and “Social Risks” with the lowest priority. The study’s practical impact lies in the innovative risk assessment method. By considering decision makers’ preferences and uncertainties, the DF FMEA approach enhances informed decision making. This contributes to a robust framework for addressing risks in the food cold chain, aiding practitioners in more effective risk management.

Outranking-based failure mode and effects analysis considering interactions between risk factors and its application to food cold chain management

Failure mode and effects analysis (FMEA) is helpful to control potential risks of enterprises by ranking failure modes based on their evaluation values regarding multiple risk factors. However, due to the cognitive limitation of experts, obtaining explicit evaluation of failure modes is difficult; in addition, existing literature on FMEA rarely considered interactions between risk factors. To bridge these research gaps, this paper proposes an outranking-based hesitant fuzzy linguistic FMEA method. Firstly, inspired by the PROMETHEE-II and the best-worst method, the outranking relation between each failure mode and the best or the worst failure mode is determined, so as to reduce the number of pairwise comparisons. Also, the hesitant fuzzy linguistic term set is used to express the ambiguity of expert judgments regarding the comparisons of failure modes. The Choquet integral is applied to integrate the scores of risk factors considering their interactions. Finally, the proposed method is implemented in an example of food cold chain risk assessment. Results shows that failure modes with high risks are the illness of cold chain workers, product loss and long-time transportation. This study contributes theories about managing ranking efficiency of failure modes in FMEA under fuzzy evaluation environment with interactive risk factors when addressing food cold chain risk evaluation problems.

Macro-environmental factors and their impact on startups from the perspective of developing countries

ABSTRACT In a global context, the development of new technologies and becomes an entrepreneurial challenge for the near future. One of the most promising phenomena of this new way are startups. This phenomenon is also emerging in developing countries like Pakistan. However, the success rate of startups is prone to several micro and macro environmental challenges and if these challenges not addressed properly, cause failure of startups. This study intends to contribute to research by proposing a methodological approach to find out the macro-environmental factors and their impact on startups from the perspective of developing countries. Firstly, the PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) analysis is conducted to identify macro-environmental factors displayed in the Cause and effect diagram. Secondly, the Fuzzy Failure Mode and Effect Analysis (FFMEA) is applied to prioritize the risks to devise strategies. Thirdly, to formulate a risk mitigation strategies and policies to support startup ecosystem. The results show that inflation rates, currency depreciation, and corruption are significant challenges for startups. Therefore, this paper aims to help policy makers, entrepreneurs and business schools to work in join collaboration to devise risk mitigation strategies for startups in emerging economies.

Automated prioritization of construction project requirements using machine learning and fuzzy Failure Mode and Effects Analysis (FMEA)

Due to the emergence of risk-based verification practices, requirement prioritization has gained importance as a task in project requirements management. However, it is challenging due to the voluminous paper-based requirements, the reliance on manual content analysis and interviews, the limited focus on specific requirement types, and the neglect of the non-conformance detectability factor. This paper leverages historical data of risk priority numbers (RPN) for many written requirements to develop a novel machine learning model for requirement prioritization. The training data includes requirement statements manually annotated with professionals' ratings for three risk factors severity, probability, and non-detectability. The overall fuzzy RPN data was aggregated using fuzzy logic to tackle uncertainty in subjective ratings. The model was trained using a Convolutional Neural Network (CNN). The study is anticipated to greatly aid professionals in prioritizing extensive textual project requirements essential for effective resource allocation that can eventually eliminate costly consequences of high-risk non-compliance.

Evidential FMEA method for human reliability assessment

Evidence theory is a useful tool for modeling and reasoning uncertain information inherent in experts’ evaluations, which is not handled efficiently in traditional failure mode and effects analysis (FMEA). This study proposes an integrated FMEA method that incorporates evidence theory and is applied to human reliability assessment. The human error information of a human-machine system in FMEA is described as a directed graph by a Bayesian network (BN) to assess the dependence among potential human-related failure modes. The BN is extended to propagate the epistemic uncertainty of FMEA team members, where belief mass is applied to model uncertainties in team members’ knowledge and to convert their subjective cognition into varying levels of uncertainty. Risk indexes for occurrence, severity and detection from multiple sources are defined as a special assessment state. The combination of the belief mass of different failure modes is performed using extended Dempster’s rules to avoid the influence of conflicting evidence. Finally, an application in the healthcare system is provided to verify the effectiveness of our model. A comparison with other fuzzy FMEA methods is also conducted, demonstrating the advantages of the proposed model in dealing with decision-makers’ epistemic uncertainty and potential failure mode interdependencies.

A novel approach for the analysis of engineering complex system accidents: The casualty of the M/V Vitaspirit

Modern sociotechnical engineering systems are comprised of dynamic and complicated man-machine-software interactions. These complex systems, such as ships, also pose a wide range of catastrophic risks. In this study, a real ship casualty that occurred in recent years was analysed using the rule-based Fuzzy Failure Modes and Effects Analysis (FFMEA). FMEA is a risk analysis technique applied before the event; however, it has been used in this paper as an accident analysis tool by handling its limitations in a fuzzy environment. A bulk carrier case was evaluated by revealing its 15 root causes with expert judgment. Then, the most critical factors are found as poor situational awareness about ship technical specifications (R7), delayed notification of the main engine cooling water leakage to the bridge (R5), and lack of reporting about the failure cannot be repaired without stoppage of the main engine (R6). Furthermore, RPN and FRPN outputs were compared and corrective actions for the critical factors offered. The purpose of this research is to demonstrate that the rule-based FFMEA is an effective method that can be applied in the accident analysis process. The findings of the study can provide significant perspectives for stakeholders to take risk-reduction measures at both onboard and company levels.

Shipboard compressor system risk analysis by using rule-based fuzzy FMEA for preventing major marine accidents

Considering the shipboard machinery, the compressed air system directly powers the ship's diesel engines' starting and routine operations. Additionally, pressurized air is distributed throughout the ship, enabling the operation of various systems. However, a compressor failure could result in the entire compressed air system collapsing, which might also result in the ship losing its maneuverability. When a ship loses its ability to maneuver, various devastating risks can arise, including grounding, collision, contact, explosion, and fire. On the other hand, compressor system risk assessment is not a widely researched subject in the field of maritime. To fill this gap, this study provides a comprehensive risk assessment of the shipboard compressor system by utilizing the rule-based fuzzy Failure Mode and Effects Analysis (FMEA) method. 33 failure modes with their causes and consequences were determined with their expert-assigned O–S-D scores. In addition, 125 If-Then rules in the fuzzy logic system were created specifically for this study. Finally, the failure modes of the shipboard compressor system were prioritized. According to the results, the most critical failure modes are identified as FM18-Broken piston rings with a 6.43 fuzzy RPN score, FM08-Piston liner wear with 6.29, and FM11-Knocking compressor operation with a 5.59. The results of this study will contribute to the maritime sector by analyzing the risks of compressor systems and introduce them to safety specialists, ship crew, shipping companies, port authorities, inspection officers, etc.

An integrated fuzzy-FMEA risk assessment approach for reinforced concrete structures in oil and gas industry

The oil and gas industry is one of the harshest environments on reinforced concrete structures. Enhancing the reliability of these industries has been identified as a critical goal to meet anticipated production targets and maintain competitiveness. The purpose of this paper is to rank and prioritize risk factors on reinforced concrete structural systems in the oil and gas industry to reduce failures and improve system reliability. The risk factors influencing reinforced concrete systems are identified based on the experts interviewed who specialized in risk analysis. In this paper, a risk assessment approach based on a hybrid fuzzy failure mode and effect analysis is developed in order to rank the factors and improve the process of reinforced concrete maintenance prioritization. The developed approach is also compared with the other two methods; namely, conventional failure mode and effect analysis (FMEA) and grey rational analysis (GRA) integrated with FMEA. The three developed approaches are designed to acquire the highest risk priority number (RPN) values; conventional RPN, GRA-FMEA RPN, and Fuzzy-FMEA RPN. These values will be utilized as the focus of improvements to reduce the possibility of some kind of failure occurring a second time and improve the deteriorated reinforced concrete structure to minimize the likelihood of failures. The results revealed that high-risk systems include the compression train, steam turbine, and combustion gas turbine generator, while the majority require maintenance of the supporting concrete foundation as soon as second-degree deterioration occurs. Furthermore, the results indicated that the Fuzzy FMEA approach was appropriate for assessing deteriorated reinforced concrete structures.. This work represents a step forward in the development of a tool that can be used to assess the risk of degraded concrete structures and improve their integrity through proper monitoring and maintenance.

Statistical Process Control (SPC) and Fuzzy-Failure Mode and Effect Analysis (F-FMEA) Approaches to Reduce Reject Products in Wine Bottle Rack Production Process at PT Alis Jaya Ciptatama

PT Alis Jaya Ciptatama (AJC) is a company engaged in the furniture industry, where the products are exported. One of the products from PT AJC is a wine bottle rack. In the production of wine bottle racks at PT AJC, many product defects were found. Therefore, it is necessary to conduct further research to determine the quality of the product, so that the correct product quality improvement is obtained. The purpose of this study was to determine the limits of statistical control and the factors causing defects in wine bottle racks so that the quality improvement provided was right on target. The methods used in this research are Statistical Process Control (SPC) and Fuzzy Failure Mode and Effect Analysis (F-FMEA). SPC method is used to determine statistical control limits and factors causing product defects. The F-FMEA method is used to determine the priority of improvement in improving the quality of wine bottle racks. The results of the research related to the statistical control limits of the SPC method were obtained that the defective data were outside the statistical control limits. While the results of research related to the causes of product failure using the SPC method are human, machine, material, environmental, method, and measurement factors. Factors causing product failure were analyzed using the F-FMEA method so that improvement priorities were obtained, namely the lack of experience of workers with an FRPN value of 269.33. Improvements that need to be made by PT AJC include providing training to mill 1 workers and splitting the logs.

Risk Analysis of Indonesian Mango Sustainable Supply Chain For Singapore Market

UD WN Mango Sultan, Gresik succeeded in exporting 1 ton of mangoes to Singapore in 2021. However, exports are still not sustainable because they are constrained by several risks faced in the mango supply chain. To support the development of mango sales, it is inseparable from good and sustainable supply chain management. Good supply chain management can reduce or anticipate the risks that are often faced by every supply chain actor. To reduce the impact of emerging risks, it is necessary to identify priority risks for further risk mitigation actions. This study aims to identify risks seen from the perception of each sustainable supply chain actor and evaluate the highest priority risk of each sustainable supply chain actor in mango commodities. The study uses primary data by conducting interviews and filling out questionnaires by informants from businessmen, farmers, and collector of mango commodity in Gresik District. Fuzzy Failure Mode and Effect Analysis (FFMEA) technique is used to determine the priority of risk mitigation efforts. The results show that there are 16 risk components for UD WN Mango Sultan and collectors, and 11 risk components for farmers. Meanwhile, the priority risks for farmers, collectors, and UD WN Mango Sultan are operational risk (5,355), social risk (7,414), and environmental risk (7,917).

A novel failure mode and effect analysis method using a flexible knowledge acquisition framework based on picture fuzzy sets

Failure mode and effect analysis (FMEA) is an effective reliability management tool for identifying potential failures in a system/component that has been widely utilized in various fields by combining fuzzy sets theory. However, the difficulty in assessment expression and acquisition, imprecision in assessment aggregation, and missing relationships among risk factors are prominent challenges in the existing fuzzy FMEA methods. Thus, this work employs the picture fuzzy sets (PFSs) theory to meet these challenges, allowing experts to express assessments more efficiently and accurately. Meanwhile, we propose a novel FMEA method to improve the three essential processes of FMEA, involving the following steps. Firstly, a flexible knowledge acquisition framework (FKAF) is established to simplify the expert evaluation process, allowing experts to express fuzzy information with various forms of non-fuzzy assessments. Then, a strategy-based picture fuzzy conversion (PFC) method is developed to standardize the non-fuzzy values to picture fuzzy numbers (PFNs). Secondly, to improve the assessment aggregation accuracy in uncertain environments, we suggest the picture fuzzy evidential reasoning (PFER) method that extend the existing fuzzy evidential reasoning (FER) methods. Thirdly, to completely describe the parallel and causal relationships among risk factors, four alternative models are established using picture fuzzy Petri nets (PFPNs) and risk priority rankings are determined through inference. Finally, the effectiveness and superiority of the proposed FMEA method are verified through two case studies and one extended experiment, demonstrating that it overcomes the shortcomings of the existing FMEA methods and reduces application costs while ensuring the rationality of rankings.

A fuzzy FMEA for detecting the risk of defects in polyethylene (PE) film products

Product quality is a top priority that must be considered so that the products produced follow quality standards based on the characteristics and specifications set by the company. This study aims to identify the failure risk of the most dominant type of defect in Polyethylene (PE) Film products and identify the significant factors that cause the most dominant defects so that a proposal for improvement is obtained. The Fuzzy Failure mode and effect analysis (FFMEA) method is applied to reduce the risk of product defects in the PE Film production process. The steps with FFMEA are: a). Identify the type of defect of the Polyethylene film product, b). investigate the potential causes of each mode of failure and their impact, c). Identify the scale and linguistic terms for the input factors S, O, and D. d). Create a membership function appropriate for S, O, and D input factors, e). Change the input factor values S, O, and D to linguistic variables, f). establish an IF-Then rule, create a fuzzy risk priority number (FRPN) membership function, g). defuzzification. The results showed that the application of this method was quite effectively applied, the risk factor for the dominant defect in the production of PE film was the type of Pinhole defect caused by material factors, and the production process and temperature with proposed improvements were focused on periodic maintenance.

Risk Evaluation of Water Environmental Treatment PPP Projects Based on the Intuitionistic Fuzzy MULTIMOORA Improved FMEA Method

The water environment treatment public-private partnership (PPP) project has a long cooperation period, large investment scale, high technical requirements, and more complex risks, which are very important to identifying and preventing risks. This paper establishes a risk evaluation model for water environmental treatment PPP projects based on the intuitionistic fuzzy Multi-Objective Optimization on the basis of a Ratio Analysis plus the full Multiplicative form (MULTIMOORA) improved Failure Mode and Effects Analysis (FMEA) method. Firstly, the risk indicators system of the water environmental treatment PPP project was constructed through the literature frequency statistics method and semi-structured interviews. Subsequently, the intuitionistic fuzzy FMEA method was used to assess the risk factors in terms of three aspects—occurrence(O), severity(S), and non-detectability(D)—and gather expert information, and the expert assessment method and deviation maximization model method were applied to assign the risk factors. Finally, Intuitionistic fuzzy Multi-Objective Optimization on the basis of a Ratio Analysis plus the full Multiplicative form (IF-MULTIMOORA) was applied to determine the risk indicator ranking and was combined with the water environmental treatment PPP project in Pingyu for example verification. The results show that the top five risk levels of PPP projects in Pingyu water environmental treatment are financing risk (changing financing conditions/high costs), market changes, government intervention and credit problems, imperfect legal and regulatory systems, and inflation. The risk assessment model proposed in this paper enables: (1) the evaluation of risk indicators from three perspectives, which is more accurate and comprehensive; (2) the introduction of intuitionistic fuzzy risk factor language variables to reasonably represent expert views; (3) the use of IF-MULTIMOORA for risk ranking to avoid the problem that RNP is the same and difficult to rank. This paper has important practical significance in promoting risk prevention and achieving the sustainable development of water environment treatment PPP projects.

Analisis Perawatan Mesin dengan Menggunakan Metode Reability Centered Maintenance dan Fuzzy Fuzzy Failure Mode and Effect Analysis

In the company there is a problem, namely the high number of downtime that occurs in the SEYI SN-110 Ton press machine at PT. Padma Soode Indonesia. The purpose of this research is to find out the right maintenance action on the system that has been selected using the RCM Reability Centered Maintenance method, to find out which components are prioritized (critical) using Fuzzy FMEA (Failure Mode and Effect Analysis), and provide policy proposals in the form of SOPs. Both methods are types of quantitative methods. The results of data collection are the number of damage to the engine inside that is as much as 40 times in 1 year with a critical percentage of 77%. This has a very big impact on the company where in 1 day the production time / shift is 8 hours, with the downtime level reaching 8 hours which can be said in 1 day there is no production. From the results of data processing using RCM to overcome downtime, namely by doing machine maintenance for 47 days so that there is no more severe downtime later. 47 days to maintain the machine starting from cleaning, replacing components whose economic value is already low and looking at the level of the machine's ability to operate, the results of the research that obtained high critical level components are engine parts with a defuzzication rate of 155. The SOP for machine maintenance uses machine maintenance time from RCM worksheet results.