Failure Mode And Effect Analysis Method Research Articles

An improved FMEA method based on the expert trust network for maritime transportation risk management

The prosperity of international trade has driven the development of shipping. Thus, evaluating shipping risks can better avoid risks, improve shipping safety, and promote economic and cultural communication and development. Failure Mode and Effects Analysis (FMEA), as a reliable technique, can be used to identify and analyse the risk of oil spills at sea and improve the stability of maritime transport. Considering the relationship among multi-experts and the influence of the leader, this study intends to improve the FMEA method from some perspectives. First, the multi-party expert trust network is constructed and the expert trust relationship is supplemented as a pre-processing of the FMEA method. Second, a expert dynamic opinion interaction model is constructed to show the evolution of risk assessment information among experts. Which demonstrates the trsut relationships among experts and the leader’s influence. Third, in this paper, the risk factor weights are determined by combining the different importance of multi-experts on risk factors and the maximizing deviation model, and the expert’s weight is determined by considering the experts’ relationships and risk assessment information. Moreover, to demonstrate the ambiguity and hesitation in expert evaluations, and show more assessment information, the hesitant fuzzy sets are employed in this paper. And the effectiveness and superiority of the improved FMEA method is demonstrated through case study and further discussion. The improvements of the FMEA method can help improve the efficiency of the risk assessment, protect the marine ecological environment, and safeguard the interests of investors.

Assessing the impact of healthcare service risks on healthcare demand under evolving economic and social structures: An improved GLDS decision making method considering risk attitudes

Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts’ influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors.

Failure mode and effects analysis method based on Fermatean fuzzy weighted Muirhead mean operator

Failure mode and effects analysis (FMEA) is an important work in product reliability design. It can identify the weak links and key items in design through fault analysis, and provide basic information for evaluating and improving the reliability of system design. In view of the shortcomings of the existing FMEA method, such as the weak ability of experts to describe and process fuzzy information and the lack of consideration of the weight and correlation between risk factors. In this paper, by fusing Fermatean fuzzy sets (FFS) and Muirhead Mean (MM) operators, we propose Fermatean fuzzy weighted Muirhead mean (FFWMM) operator and develop a new FMEA method. Firstly, two new risk factors are proposed to overcome the shortcoming of insufficient consideration of the FMEA method. By introducing FFS, the ability of experts to express fuzzy information is enhanced. Using FFWMM operator to aggregate evaluation information can deal with the weight and correlation between influencing factors well. Finally, an example is given to illustrate the superiority of the proposed method.

An Improved Failure Mode and Effect Analysis Model for Automatic Transmission Risk Assessment Considering the Risk Interaction

Failure mode and effect analysis (FMEA) is a powerful tool for reliability management that has been widely applied in a variety of areas. The interaction relationship exists in the whole process of FMEA risk assessment, and has an important impact on it. However, few scholars consider the interaction among experts, risk factors, and failure modes in the FMEA framework. Therefore, we propose an improved FMEA method, which comprehensively considers the interaction between each part. First, the Choquet integral is used to improve the incomplete part of the social trust network, then the importance of experts is determined. Second, the Shapley-LINMAP linear programming method based on cloud model is proposed to calculate the weight of risk factors. Finally, the improved decision-making trial and evaluation laboratory method based on cloud model is used to rank the failure modes. By applying the method proposed in this article to the case of automatic transmission (AMT) of new energy vehicles, the effectiveness and superiority can be verified from the comparison analysis with other methods, which enriches the theoretical research of the FMEA method and improves the reliability of risk assessment of AMT.

A New Extended Approach to Reduce Admission Time in Hospital Operating Rooms Based on the FMEA Method in an Uncertain Environment

Operating rooms (ORs) are one of the essential hospital resources and optimal management can result in efficient OR usage. The admission time reduction before surgery in the ORs can lead to on-time surgery and efficient use of ORs. This study addresses this issue by identifying the main failure modes that cause delays in ORs. The conventional methodology known as Failure Mode and Effects Analysis (FMEA) represents one of the prevailing techniques utilized for the purpose of ascertaining failure modes within a given process. This involves the assignment of numerical scores to each failure mode, with the intention of utilizing the resultant Risk Priority Number (RPN) to facilitate the identification of said failure modes. However, RPN scoring has been criticized for some deficiencies. This study proposes a three-phase approach to address some of the shortcomings of the FMEA method. The initial stage involves utilizing the FMEA approach to recognize failure modes and assess the crucial elements of RPN. Following this, the second stage employs the Z-BWM technique and expert insights to determine the weights of the five essential factors. Lastly, in the third phase, risks are prioritized using the proposed Z-ARAS method based on the outputs of the previous phases. This approach considers the uncertainty in the determining factors and assigns different weights to them, while also taking into account the reliability of the risks through the Z-Number theory. Finally, comparing the proposed approach with other traditional approaches, reinforces the usefulness of the proposed method in evaluating failure modes in OR management.

Probabilistic double hierarchy linguistic risk analysis based on failure mode and effects analysis and S-ARAS method

Failure mode and effects analysis (FMEA) is regarded as useful and efficient tool in identifying and eliminating system failures and important means in safety and reliability analysis. It is widely utilized in diverse areas for improving and enhancing the performance of systems. However, in the procedure of risk assessment, traditional FMEA method usually ignores the influence of weights. Simultaneously, when represent risk assessment information, fuzzy and complex information cannot be represented accurately. Besides, psychological behaviors of decision-makers cannot be considered. Thus, this paper aims to present a new method based on FMEA and S-additive ratio assessment (S-ARAS) method to address the marine risk assessment problems under probabilistic double hierarchy linguistic (PDHL) environment. Firstly, the probabilistic double hierarchy linguistic term sets (PDHLTSs) are utilized to express the evaluation information of failure modes. The expert evaluation information of failure modes and subjective weight is also assessed by using PDHLTSs. The PDHLTSs can describe the information more reasonably and precisely. Secondly, FMEA method is used to evaluate these alternatives from three aspects of Severity (S), Occurrence (O) and Detection (D). Then, the bounded rationality of decision-makers (DMs) is considered. So, the prospect theory and ARAS method is combined to evaluate the risk problems. Meanwhile, comprehensive weight determination method is established based on the social network analysis (SNA) and The Integrated Determination of Objective CRIteria Weights (IDOCRIW) method under PDHL environment. The integrated method is more flexible in determining the importance of failure modes and avoids the randomness of subjective evaluation. Finally, the newly proposed method is applied to address the marine risk assessment problem. Sensitivity analysis of parameter and weight methods is carried out. Furthermore, comparison analysis is given to illustrate the superiority of the presented method.

A novel FMEA approach based on probabilistic linguistic best-worst method and TOPSIS with application to marine diesel fuel injection system

The failure mode and effect analysis (FMEA) is an effective tool to analyze risks and potential effects of complex systems, and it is one of the most widely used risk analysis methods for complex systems as there often exists various factors that could affect the operation of the complex systems. Conventional FMEA methods have been limited to using crisp values to represent the assessments, which has been criticized for having many deficiencies. Marine diesel fuel injection system is an important part of marine diesel engine, and its failure could directly affect the performance of the marine diesel engine and even impact the safe operation of the ship. However, little attention has been paid to the FMEA of the marine diesel fuel injection system. To this end, this paper presents a novel FMEA method based on the best-worst method (BWM) and TOPSIS method with probabilistic linguistic term set (PLTS). Firstly, the PLTS is used to represent the uncertain and linguistic judgments of experts. Then, the BWM is extended with PLTS to determine the weights of different elements for FMEA, and the TOPSIS is extended with PLTS to assess and rank different failure modes. Finally, a case study on marine diesel fuel injection is presented, and the most critical failures are identified for improvement measures. The results show that the proposed method could help managers and engineerings identify the most important failure modes for marine diesel fuel injection system.

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.

ANALISIS KUALITAS PRODUK PENYAMAKAN KULIT DENGAN METODE SQC dan FMEA

PT Adi Satria Abadi is a company engaged in tanning. The problem faced by the company is the presence of defective leather during the goat and sheep tanning production process. From these problems, the purpose of this study was to identify the level of product defects, analyze the factors that cause defects, and make recommendations for improvements to improve the quality of goat and sheep tanning products. Data processing in this study uses the SQC (Statistical Quality Control) and FMEA (Failure Mode and Effect Analysis) methods. Based on the identification results, it was found that there were 1,507 holes for holes, 1,066 for thick defects, 1,061 for louse defects, and 1,086 for size defects. The results of the SQC analysis still have uncontrolled data based on the control chart which is on size defects with a total of 1,086 defects, while for hollow defects, too thick defects, and tick defects are under control. The results of the FMEA analysis show that the order of completion with the first priority is spotting bugs with an RPN value of 36. The suggested improvements to the company are by directly selecting raw materials and supervising employees.
 
 Keywords: Quality, Product Defects, Seven Tools, SQC, FMEA.

АДАПТАЦИЯ МЕТОДА ОЦЕНКИ РИСКОВ ПРИ ЭКСПЛУАТАЦИИ СБОРНОГО РЕЖУЩЕГО ИНСТРУМЕНТА С УЧЕТОМ ВЫЯВЛЕНИЯ ВОЗМОЖНОСТЕЙ

A variety of risks arises during the operation of a collecting cutting tool, which can lead to a decrease in its output. To increase the serviceability of the tool in the process of cutting it is necessary to identify, evaluate and, if possible, to minimize the negative impact of the consequences of one risks or another. For this purpose, an analysis of risk assessment methods in the context of industrial enterprises was conducted. As a result the most effective and popular in various industries solutions were identified. During consideration of one of such methods – failure mode and effects analysis (FMEA) – an overview of its current changes is presented. In addition, the author proposes her own method based on the FMEA method for risk assessment during the operation of collecting cutting tool, which includes, among other things, the detection of opportunities and their numerical interpretation.

Risk assessment of bolt-gasket-flange connection (BGFC) failures at hydrogen transfer stations based on improved FMEA

Failure Modes and Effects Analysis (FMEA) is an engineering technique widely used to identify, evaluate, and eliminate known or potential failures, problems, and errors and inform risk management decisions. Traditional FMEA uses the risk priority number (RPN) to identify potential failure modes and their levels. However, it displays shortcomings in determining risk factor weights, ranking risk priorities of failure modes, and dealing with uncertainties during the risk assessment process. To address these issues, this paper proposed an improved FMEA risk assessment method by integrating FMEA, the analytic hierarchy process (AHP), the technique for order preference by similarity to an ideal solution (TOPSIS), and the cloud model (CM) to determine a more reasonable failure mode level. First, the CM was used to improve the FMEA framework structure to comprehensively assess ambiguity and randomness during the evaluation process. Second, the cloud weights of the risk factor severity (S), occurrence (O), and detectability (D) were calculated using AHP and CM to determine the relative risk factor importance. Then, the cloud distance measurement algorithm and TOPSIS were combined into the TOPSIS-CM risk ranking method to objectively identify the failure modes requiring improvement. Finally, a case study of a flange connection system at a hydrogen blending transmission site in northern China was conducted to verify the adaptability of the proposed method in failure mode assessment. Compared with traditional FMEA methods, the decision scores obtained using the improved FMEA were more discriminating and distinguished the risk of each failure mode, providing more accurate information to decision makers (DMs).

New Generation FMEA Method in Automotive Industry: An Application

It is aimed to gain study substantiality by making an application for risk analysis study in the automotive industry. To bring to the literature the current point of risk analyzes used in FMEA methods applied in the automotive industry. At the same time, it is aimed to improve a problem experienced in the process of an operating in the automotive supplier industry with the risk analysis method. Failure Mode and Effects Analysis (FMEA) from risk analysis which is strong method to identifying and managing of the risks of potential failures occurring from product, process, service and system. Three methods were used in FMEA risk analysis in the study. AIAG FMEA Handbook fourth edition 2008, VDA-4 standard and new generation AIAG&VDA FMEA handbook, 2019. In this study, a Process FMEA study was prepared for the surface coating preparation process used by an automotive supplier industry. According to the results of the risk analysis, corrective actions were defined and prepared after the P-FMEA study. Afterwards, the necessary investments were made by the company management and the risks were minimized. With this investment, an innovative and technological application was integrated by ensuring the transition of the process from the human factor to automation. In addition, differences and current information about FMEA evaluation methods used in the automotive industry have been brought to the literature

ANALISIS PENGENDALIAN KUALITAS CACAT DIMENSI PADA HEADER BOILER MENGGUNAKAN METODE FMEA DAN FTA DI PT. IHI POWER SERVICE INDONESIA (IPSI)

IHI Power Service Indonesia is a company engaged in the Boiler Pressure Part Industry (Fabrication) in every production activity it always strives to produce quality products according to ASME standards (American Society of Mechanical Engineers). Production results are less than the maximum. In the 2020 period the total production was 28,527 tons and the number of defects was 364 tons, in the 2021 period the total production was 22,703 tons and the number of defects was 301 tons. The author uses the Failure Mode And Effect Analysis (FMEA) method and the Failure Tree Analysis (FTA) method. From the results of data processing using a Pareto diagram based on the RPN value obtained from the FMEA method, the dominant type of defect is obtained, namely Hole Distance with an RPN value of 364.5 and Dimension Length with a value of 351.17, then analyzed using FTA, at the proposed improvement stage it is proposed to use the 5W+1H method. Factors that cause defects include inaccurate and faded measuring instruments, rare maintenance on radial drilling machines and blades that are rarely replaced. Proposed improvements to reduce defects include requesting replacement of measuring instruments, carrying out regular maintenance of radial drilling drilling machines, holding shift schedules for operators, conducting training for operators.

Application of FMEA methods in Minh Duong furniture company in Vietnam

This paper aims to apply FMEA (Failure Mode and Effect Analysis) tools in the wood production process of Minh Duong Wood Company. In this study, the process evaluation indicators according to FMEA were analyzed such as failure severity - S (Severity value), failure frequency - O (Occurrence number), error detection ability. failure - D (Detection number), risk priority factor - RPN (Risk Priority Number). The research results show that the failure modes of the process have been systematically and comprehensively identified. Fault types are ranked as priority for improvement and corresponding solutions have been proposed.

Vehicle fault diagnosis using FTA and FMEA method

In recent years, users requirements for the richness of car functions have gradually increased. The application of a large number of electronic components in the automobile makes the degree of complexity of the automobile higher, so the user has a higher demand for the stability of the automobile system. Especially once a key functional component fails, driving safety will be seriously affected. Because in the design process, we find and analyze the possible causes of failure, so as to avoid the impact of failure in the design as much as possible. We use FMEA (Failure Mode and Effect Analysis) Analysis method and FTA (Fault Tree Analysis) analysis method to analyze the car headlight fault. These two analysis methods can efficiently and accurately analyze faults and determine the most basic events leading to failure. Based on the above analysis results, the proposed solution to improve the system can be given, so that the system can meet the functional safety requirements.

Quality Control on Bogo Helmet Coating Process Using the Six Sigma Method, Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA)

Hanesda Assembling Helmet is a private company engaged in the assembly of bogo helmets. One of the serious problems faced by this company is that the defective products produced in the production process reach 4% of the tolerance limit set by the company, which is 1.5%. The purpose of this research is to control the quality of the coating process which has a lot of product defects. The method used in this study is the Six Sigma method with the stages of DMAIC. The method used to analyze the causes of failure is the FTA method and the method used to determine the weighting in prioritizing the repair design is the FMEA method. The results of the research using the Six Sigma method are obtained DPMO values of 19,931 with a Sigma level of 3.57. Types of defects that have the highest RPN are streak defects, flex defects, uneven paint defects and paint runs defects with an RPN value of 112. High RPN value will be prioritized for improvement plans.

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.

Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory.

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster-Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.

Risk assessment in machine learning enhanced failure mode and effects analysis

PurposeThe traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).Design/methodology/approachThis work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.FindingsThe results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.Originality/valueThe proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

Perbaikan Kualitas Pengalengan Ikan Dengan Metode FMEA di PT. Sumber Mutiara Samudra Banyuwangi

Canning is a modern preservation method by heating treatment using high temperatures and packaging into hermetic cans. The conditions in the cans are required to be in a vacuum to prevent the growth of spoilage microbes that can damage the product. The hermetic packaging can be obtained by doing a perfect closure and there is no failure that occurs during the can closing or seaming process using a seamer machine. The types of seaming defects that occur at PT. Sumber Mutiara Samudra Banyuwangi are drop vees, false seams, and leaks. This quality control is carried out in the hope of minimizing the occurrence of defects during the production process. The FMEA (Failure Mode And Effect Analysis) method is one method that can be used to minimize the occurrence of can defects in the 155 gram fish canning process at PT. Sumber Mutiara Samudra Banyuwangi.