Fuzzy Failure Research Articles

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.

Transformer maintenance decision based on condition monitoring and fuzzy probability hybrid reliability assessment

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment.

Enhanced Adaptive Kriging Method for Estimating Fuzzy Failure Probability with Profust Model

Aiming at analyzing the structural fuzzy failure probability with probability inputs and fuzzy-state assumption (profust model), an adaptive kriging model-based sequentially truncated Monte Carlo simulation (MCS) method is investigated, where estimating the fuzzy failure probability can be transformed into identifying the absolute safety domain, the absolute failure domain, and the exact performance function values of the fuzzy safety–failure transition domain. To this end, in this study, three kriging models are sequentially constructed to realize the three aims, respectively. To further improve the efficiency of adaptively updating kriging model, adaptive radial-based importance sampling technique is embedded to divide the whole MCS candidate sampling pool (CSP) into several sub-CSPs. Then, the kriging model is updated sequentially in each sub-CSP instead of the whole CSP, and the samples dropped into the optimal hypersphere of the absolute safety domain can be truncated and do not participate in the CSP of updating the kriging model so that the size of whole MCS CSP and the size of CSP in each learning process of updating kriging are reduced simultaneously. As a result, the efficiency of estimating the fuzzy failure probability is enhanced. The results of two examples verify the effectiveness of the proposed method.

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 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.

Layer-specific evaluation-based Hierarchical Fuzzy Failure Mode and Effect Analysis

Reliability and safety have always been the main focus while developing critical automotive systems such as brakes. Failure Modes and Effect Analysis (FMEA) is one of the primary systematic reliability analysis tools. Due to the arising uncertainties and subjectivity, in models such as this one the fuzzy approach is highly popular, i.e., the fuzzy rule-based risk assessment methods can be used to model and depict the subjective opinions of estimators mathematically. In this paper, the authors propose a methodological approach to the implementation of fuzzy rule-based Hierarchical FMEA (H-FMEA), where the membership functions are different depending on the layer. Moreover, the information between each layer is transferred in the form of fuzzy numbers instead of crisp values, in order to further improve the reliability of the system.

Design and process optimization of combined medical and elderly care services: An integrated service blueprint-TRIZ model.

China's increasingly aging population is resulting in an imbalance between supply and demand for elderly care resources. The theory of “combined medical and elderly care” (CMEC) has introduced a new perspective in the conception of China's elderly care problems. This study employed the service blueprint, fuzzy failure mode and effects analysis (Fuzzy-FMEA), and the theory of inventive problem solving (TIPS or the Russian acronym TRIZ) for the process optimization of CMEC services in three phases. In the first phase (service process analysis), potential service failure points in the service process were analyzed using the service blueprint technique. In the second phase (service failure diagnosis), Fuzzy-FMEA was applied to diagnose the service failure modes and explore the possible causes and effects. The service failure modes were then prioritized based on fuzzy numbers and the cumulative fuzzy risk priority number (Fuzzy-RPN). Finally, in the third phase (generation of service optimization solutions), the TRIZ parameters, inventive principles, and contradiction matrix were first employed to select TRIZ inventive principles. The selected TRIZ inventive principles were then used to inspire inventive solutions for new service processes. Finally, a case study was conducted on the service processes of elderly care institutions to demonstrate the applicability of the optimization solutions.

Optimization of Logistics System with Fuzzy FMEA-AHP Methodology

The COVID-19 pandemic broke out and the global logistics industry suffered severe losses; therefore, the Fuzzy FMEA-AHP (Fuzzy Failure Mode and Effects Analysis-Analytic Hierarchy Process) method is proposed to analyze the failure reasons of the logistics system in the COVID-19 pandemic. In this article, we have made an optimization on the basis of the FMEA method: the fuzzy is integrated into the FMEA algorithm, referred to as F-RPWN (fuzzy risk priority-weighted number). Meanwhile, the AHP is used to determine the weights of risk indicators. In this article, we consider new logistics failures, such as the failure modes and failure reasons of the logistics system under the COVID-19 pandemic. There are 12 failures that have been determined, and relevant preventive and corrective measures have been recommended to cut off the path of failure propagation and reduce the impact of failures. In addition, the proposed method can help logistics firms, their supply chain partners, and customers with risk management issues during the COVID-19 pandemic.

FMEA Method Using Spherical Fuzzy Sets for Risk Analysis of the Tech Startup

Introduction: Tech startups are fast-growing businesses that target the demands of the marketplace by developing innovative products, services, or platforms. Startups ensure socially, economically, or environmentally more effective alternatives by using or by creating appropriate technologies. Many factors have become prominent regarding the success and sustainability of the product or service offered by the startup: investment, experience, and education of the team, the leadership of the management, creativity, innovation, technological breakthroughs, surrounding community, future perspective, target marketing strategy, location and the analysis of the market, etc. But since 80% of startups do not survive after five years, defining the important risk factors is crucial to develop the right strategies for successful startups. In this study, the risk factors have been defined based on the business model, which has an important place in the success of the technology startups, which use technology intensively. Comprehensive risk analysis on identified factors is presented to identify effective managerial strategies for technology startups to not fail. Methods: Spherical Fuzzy Failure Mode and Impact Analysis (SFFMEA) was used within the framework of a business model canvas for risk analysis for the failure of technology startup projects. Due to the lack of recorded data for analysis, the opinions of field experts were used. While the business model canvas guided the identification of detailed risk factors, FMEA enabled the risk analysis of factors that cause startup projects to fail, and considering parameters related to the probability of the relevant risk factors, their impact on the failure of the project, and the detection level of the risk factor. Spherical Fuzzy, on the other hand, allowed the quantitative inference of FMEA's comprehensive parameter definitions associated with the risk factors through experts. Thus, all risk factors that may cause the failure of tech startups were ranked according to their risk priority numbers (RPNs), with the SFFMEA analysis, which offers a comprehensive risk analysis. Results: The findings show that the most important causes of the tech startup’s failure are “noncompliance with existing restrictions”, “inappropriate venture capital strategy”, and “lack of clustering support”. Conclusion: These failure modes can be interpreted according to their frequency of encounter, potential effects, and detectability, and can be considered an important finding in the development of appropriate managerial strategies for the mitigation of the risk factors so the startups can survive in their first five years. Also, with the proposed risk analysis methodology, a comprehensive analysis of any startup project can be performed according to its conditions and characteristics.

Human error identification and risk prioritization in LPG unloading operations

Objectives. Errors due to human activities in any operation are analyzed using human reliability analysis in which the principal step is to identify potential human errors followed by quantification and analysis of the error. This work intends to apply a methodology for identifying human errors and to prioritize the risk associated with them in a liquefied petroleum gas (LPG) unloading operation. Methods. The methodology uses hierarchical task analysis which provides the basic framework, along with a systematic human error reduction and prediction approach which aids in identification and categorization of the errors associated with each task with the help of predefined error taxonomy. Also, in order to quantify the risk associated with each identified error, fuzzy failure mode and effect analysis has been adopted. To rank and prioritize the risk associated with each identified error where the individual constituent components are non-commensurable in nature, the VIseKriterijumska Optimizacija I Kompromisno Resenje method has been incorporated. Results and conclusions. Applicability of the methodology presented will help comprehend the severity of risk corresponding to each error at different levels, and the ranking mechanism thus developed in this work aids to prioritize the action to minimize the likelihood of errors.

Availability and Reliability Analysis of a k-Out-of-n Warm Standby System with Common-Cause Failure and Fuzzy Failure and Repair Rates

In the real life, there exists limited information or uncertainty in knowledge about failure and repair rates which follow one of the standard distributions as exponential distribution and Weibull distribution with some parameters. We suppose that these parameters are fuzzy which allow one to specify a system design for a “worst-case scenario.” In this paper, the fuzzy availability and the fuzzy reliability of a redundant repairable parallel k-out-of-n warm standby system with common-cause failure are evaluated. We assume that the failure time of each operating unit or warm standby unit follows Weibull distribution with two fuzzy parameters and the repair time of any failed unit follows exponential distribution with one fuzzy parameter. Each fuzzy parameter is represented by triangular membership function estimated from statistical data taken from random samples of each unit. Also, we give a numerical example for a fuzzy repairable parallel 3-out-of-5 warm standby system with two active and three warm standby units to get analytically and represent graphically the fuzzy availability and reliability function of this fuzzy system.

Sustainability Management Model Based on Risk Analysis and Implementation of the Model

In modern business management, the effective management of organizational structures based on goals within the scope of modern management concepts, as well as the continuous improvement of processes for increasing competitiveness, are critical. The purpose of this study is to discuss the matter of risk identification and analysis, practices that are seen in many different fields within the scope of the notion of sustainable management. It aimed to add the dimensions of targets and risk analysis to the scope of process analysis and development, within this study. The stages, starting with the definition of the processes in the process model through the targets based on the risk analysis, are transformed into a quality assurance cycle and completed with the determination, monitoring, and evaluation of the sustainable process development activities. The performance indicators of the business processes in a textile business were determined, and the identification of a model structure with systematic traceability was provided, in practice. The opportunities and threats encountered in achieving the determined goals were identified. The defined factors were examined within the scope of the failure mode and effects analysis and risk analysis methods. The risk evaluations were performed using fuzzy numbers. The results obtained by fuzzy failure mode effects analysis were evaluated through the MATLAB Fuzzy Logic Design Toolbox model. Risks were ranked as a result of the evaluation, and process development activities were chosen.

Effect of fuzzy failure criterion on probabilistic seismic risk analysis under multidimensional performance limit state

Traditional probabilistic seismic risk analysis (PSRA) often adopts accurate limit state boundary to define the structural performance limit state, which ignores the fuzziness of limit state thresholds and boundary. In this paper, considering the fuzzy failure criterion, a novel PSRA method based on fuzzy-random hybrid model is proposed under multidimensional performance limit state. A multiple integral formula based on fuzzy failure criterion is established for PSRA, and the influence of different membership functions and membership intervals are studied. A reinforced concrete (RC) frame-shear wall structure is established as the numerical example. The results show that: Fuzzy failure criterion will lead to the change of failure probability, thus the structural potential seismic risk or capacity could be analyzed. With the increase of structural damage degree, the influence of fuzzy failure criterion is become larger. Different membership functions have different effects on vulnerability curves and capacity hazard surface. With the expansion of membership interval, the influence of fuzzy failure criterion on PSRA is getting larger.

Fuzzy failure mode and effect analysis model for operational supply chain risks assessment: an application in canned tuna manufacturer in Thailand

Fuzzy failure mode and effect analysis model for operational supply chain risks assessment: an application in canned tuna manufacturer in Thailand

Service Failure Risk Assessment and Service Improvement of Self-Service Electric Vehicle

Electric vehicle sharing is necessary for achieving carbon neutrality. The self-service electric vehicle mode offers unique advantages in terms of freedom of movement and privacy protection. Meanwhile, this mode requires a high-quality service guarantee because of the separation of management and use. The purpose of this study is to propose a framework for the risk control and service optimization of self-service electric vehicles, which includes service life cycle analysis, risk assessment by using a newly integrated fuzzy failure mode and effect analysis, and a consumer satisfaction survey based on the Kano model. Sixteen services were extracted through the service life cycle analysis and online review study, and their corresponding service failures were then ranked through risk assessment. The risk assessment showed that the reliability of vehicle-related services has the greatest impact on safety, followed by financial-related and driving-safety-related services. A Kano model-based survey showed that all kinds of service failures brought significant customer non-satisfaction, while different service improvements brought differentiated satisfaction. To deeply improve service satisfaction, a Risk-Satisfaction analysis was conducted, indicating that services with high risk and high satisfaction deserve further investment.

A fuzzy project buffer management algorithm: a case study in the construction of a renewable project

One of the major problems with projects is that they are not completed according to schedule. Uncertainty always exists at the heart of real-world project scheduling problems. This paper introduces a fuzzy project buffer management (FPBM) algorithm which is a combination of the adaptive procedure with resource tightness (APRT) and fuzzy failure mode and effects analysis (FFMEA) methods. This paper aims to present an efficient model for project buffer sizing by taking FFMEA into account to reach a more realistic schedule. In this research, for increasing the efficiency of the APRT method, the FFMEA technique is simultaneously applied with them. This research was carried out as a case study in a renewable energy (RE) project. The methodology of this research consists of two phases. The first phase is the implementation of the APRT buffer sizing method. In the second phase of the research methodology, the fuzzy FMEA method is implemented. To validate the proposed model, the results are compared to several buffer management models proposed recently. Also, the results were compared with the results of similar projects. The findings show that considering the fuzzy FMEA technique in the APRT method, a more realistic schedule was obtained in this project.

FMEA in Smartphones: A Fuzzy Approach

Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results.

Fuzzy-FMECA Decision-Making Tool for Assessment and Analysis of Performance of Urban Sewerage Networks

Sustainable and effective management of urban sewerage networks (USNs) require relevant, appropriate, and effective decision support tools. This article proposes an innovative decision-making methodology, namely, the fuzzy failure modes, effects, and criticality analysis (FMECA) method, based on the fuzzy logic of risk, severity, and detectability indexes as defined in FMECA. Fuzzy-FMECA is based on rules that connect the concepts of inductive reasoning, logical implications for inferred knowledge of USNs using linguistic or numerical descriptions of the risk, gravity, and detectability indexes. The application of the proposed methodology to a practical case study of the USN of Oued M’Kacel in the city of Algiers, Algeria, proves its effectiveness. Furthermore, the performance of USNs from the proposed fuzzy-FMECA is compared to the performance from FMECA. The results show that the fuzzy-FMECA method can help sewerage managers assess the rehabilitation and prioritization of USNs in a more effective way.

Development and Simulation of Cyberdisaster Situation Awareness Models

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.

Service design touch point failure model and effect analysis based on the triangular fuzzy number evaluation method

How to improve employees' satisfaction in the remote office mode while working together has become a challenge for enterprises to deal with the new office management mode. The concept of service design touch point is introduced into the remote office management system to improve employees' office experience and help enterprises complete the remote office service design. From the perspective of virtuous circle, the cost of failure analysis and preventive measures for service design touch point in the process of service design is lower than that of dealing with failure after service failure. Therefore, this paper made a risk assessment on the failure mode of service design touch point loss analysis method based on the triangular fuzzy number evaluation method. In the failure risk assessment, the fuzzy failure mode and effect analysis theory and the failure mode of service design touch point are analyzed first, and the expert scoring method is used to evaluate and determine the fuzzy level of severity, occurrence and detection of each failure mode; Then, aiming at the ability difference of different experts in evaluating different objects, the expert importance matrix, which is based on attribute difference, is determined; On this basis, the risk priority value of each failure mode is analyzed and determined by fuzzy calculation method. The risk evaluation ranking results of touch point failure modes are obtained according to the risk priority value. Finally, taking the failure analysis of Y company's remote collaborative office service as an example, the feasibility of the method is verified by the case of protection during COVID-19.