Improved FMEA based on IVF and fuzzy VIKOR method: A case study of workpiece box system of CNC gear milling machine

Failure mode and effect analysis (FMEA) is a tool used to define, identify, and prevent known or unknown potential risks. An improved FMEA based on interval triangular fuzzy numbers (IVF) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is proposed in this study to solve problems of expression and processing of uncertain information, weights of risk factors, and ranking of failure modes in traditional FMEA. Linguistic variables are used to evaluate failure modes level and relative importance of risk factors and are expressed via interval-valued triangular fuzzy number. Determining the subjective weights of risk factors using fuzzy AHP, calculating the objective weights of risk factors using the extended VIKOR method, and obtaining the comprehensive weights of risk factors via ICWGT are proposed for solving the weight problem of risk factors. Finally, the fuzzy VIKOR method is used to rank risk priority of failure modes. The proposed method is used to evaluate workpiece box system of CNC gear milling machine and the results are compared with the findings of other methods to verify effectiveness and rationality of the proposed method.     

基于改进的TOPSIS法与RSR法的质量改善项目综合评估

针对失效模式与影响分析中的风险优先数（RPN）方法在质量改善项目排序问题上的局限性,提出一种新的排序方法。首先,借助模糊集理论和熵权法,确定决策指标的模糊指标值和主、客观权重;随后采用改进的理想解逼近法（TOPSIS）与秩和比法（RSR）获得优先级排序和分档;同时,提出并定义了相对应的关键术语：改善主题、难易程度、发生度和改善潜力,最后,结合企业实例验证了新排序方法的有效性和稳定性。     

A linguistic risk prioritization approach for failure mode and effects analysis: A case study of medical product development

Failure mode and effects analysis (FMEA) is a widely used risk management technique for identifying the potential failures from a system, design, or process and determining the most serious ones for risk reduction. Nonetheless, the traditional FMEA method has been criticized for having many deficiencies. Further, in the real world, FMEA team members are usually bounded rationality, and thus, their psychological behaviors should be considered. In response, this study presents a novel risk priority model for FMEA by using interval two‐tuple linguistic variables and an integrated multicriteria decision‐making (MCDM) method. The interval two‐tuple linguistic variables are used to capture FMEA team members' diverse assessments on the risk of failure modes and the weights of risk factors. An integrated MCDM method based on regret theory and TODIM (an acronym in Portuguese for interactive MCDM) is developed to prioritize failure modes taking experts' psychological behaviors into account. Finally, an illustrative example regarding medical product development is included to verify the feasibility and effectiveness of the proposed FMEA. By comparing with other existing methods, the proposed linguistic FMEA approach is shown to be more advantageous in ranking failure modes under the uncertain and complex environment.     

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.     

一种综合赋权的改进FMEA风险评估方法

针对传统故障模式和影响分析(failure mode and effects analysis, FMEA)方法中的未考虑风险因子权重以及风险因子权重难确定这一问题,提出一种综合赋权的改进FMEA风险评估方法。该方法首先通过FMEA团队明确评估对象和FMEA范围,然后列出所有潜在故障模式,对故障模式进行打分,得到所有的专家打分评估表,再通过语言变量转化为直觉模糊数。由层次分析法确定主观权重,由数据本身确定客观权重,使用直觉模糊混合加权算子(intuitionistic fuzzy hybrid weighted, IFHW)算子集结评价信息,得到所有的故障模式的得分函数,最后基于风险最大化选取每个故障模式的最大分数,进行排名,得到最终的故障模式风险顺序。通过对静电纺丝设备进行FMEA分析,并与其他方法进行比较,验证了所提方法的可行性。     

Failure mode and effect analysis using the hesitant intuitionistic fuzzy hybrid GRP approach with ordered comprehensive weights

Failure mode and effect analysis (FMEA), a multidisciplinary reliability analysis tool based on team evaluations, has been widely used in various industries. There are three critical issues in FMEA: the conversion of linguistic evaluations, the weights of risk factors, and the ranking mechanism of failure modes. Scholars have used various fuzzy theories and multi-attribute decision-making (MADM) methods to improve traditional FMEA, but there are still deficiencies. In this paper, the hesitant intuitionistic fuzzy set (HIFS), a concept that combines the intuitionistic fuzzy set (IFS) and the hesitant fuzzy set (HFS), is introduced into FMEA to convert linguistic evaluations. Some operators based on HIFS are proposed to process the converted data. Among them, a hesitant intuitionistic fuzzy comprehensive weighted Hamming distance (HIFCWHD) operator is proposed to compute the ordered comprehensive weight, effectively weakening the effect of extreme scores on results. The gray relational projection (GRP) method is adopted to determine the risk priority order of the failure modes. Finally, we give an illustrative case to demonstrate the effectiveness of the proposed FMEA method.     

An alternative evaluation of FMEA: Fuzzy ART algorithm

Failure Mode and Effects Analysis (FMEA) is a technique used in the manufacturing industry to improve production quality and productivity. It is a method that evaluates possible failures in the system, design, process or service. It aims to continuously improve and decrease these kinds of failure modes. Adaptive Resonance Theory (ART) is one of the learning algorithms without consultants, which are developed for clustering problems in artificial neural networks. In the FMEA method, every failure mode in the system is analyzed according to severity, occurrence and detection. Then, risk priority number (RPN) is acquired by multiplication of these three factors and the necessary failures are improved with respect to the determined threshold value. In addition, there exist many shortcomings of the traditional FMEA method, which affect its efficiency and thus limit its realization. To respond to these difficulties, this study introduces the method named Fuzzy Adaptive Resonance Theory (Fuzzy ART), one of the ART networks, to evaluate RPN in FMEA. Copyright © 2008 John Wiley & Sons, Ltd.     

A rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments

This study aims at improving the effectiveness of failure mode and effect analysis (FMEA) technique. FMEA is a widely used technique for identifying and eliminating known or potential failures from system, design, and process. However, in conventional FMEA, risk factors of Severity (S), Occurrence (O), and Detection difficulty (D) are simply multiplied to obtain a crisp risk priority number without considering the subjectivity and vagueness in decision makers’ judgments. Besides, the weights for risk factors S, O, and D are also ignored. As a result, the effectiveness and accuracy of the FMEA are affected. To solve this problem, a novel FMEA approach for obtaining a more rational rank of failure modes is proposed. Basically, two stages of evaluation process are described: the determination of risk factors’ weights and ranking the risk for the failure modes. A rough group ‘Technique for Order Performance by Similarity to Ideal Solution’ (TOPSIS) method is used to evaluate the risk of failure mode. The novel approach integrates the strength of rough set theory in handling vagueness and the merit of TOPSIS in modeling multi‐criteria decision making. Finally, an application in steam valve system is provided to demonstrate the potential of the methodology under vague and subjective environment. Copyright © 2013 John Wiley & Sons, Ltd.     

Modified failure mode and effects analysis using approximate reasoning

The marine industry is recognising the powerful techniques that can be used to perform risk analysis of marine systems. One technique that has been applied in both national and international marine regulations and operations is failure mode and effects analysis (FMEA). This risk analysis tool assumes a failure mode, which occurs in a system/component through some failure mechanism; the effect of this failure is then evaluated. A risk ranking is produced in order to prioritise the attention for each of the failure modes identified. The traditional method utilises the risk priority number (RPN) ranking system. This method determines the RPN by finding the multiplication of factor scores. The three factors considered are probability of failure, severity and detectability. Traditional FMEA has been criticised to have several drawbacks. These drawbacks are addressed in this paper. A new proposed approach, which utilises the fuzzy rules base and grey relation theory is presented.     

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision‐making domain. Recent improvements on the FMEA have largely focussed on addressing the shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the risk priority number metrics, that is occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data are sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure of which the posterior function combines both prior functions elicited from experts and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability is demonstrated in the case study of thermal power plant equipment failures. Copyright © 2016 John Wiley & Sons, Ltd.     

An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory

mode and effects analysis (FMEA) is an effective tool to assess the risk of a system or process under uncertain environment. However, how to handle the uncertainty in the subjective assessment is an open issue. In this paper, a novel method to deal with the uncertainty coming from subjective assessments of FMEA experts is proposed in the framework of Dempster–Shafer evidence theory. First, the uncertain degree of the assessment is measured by the ambiguity measure. Then, the uncertainty is transformed to the reliability of each FMEA expert and the relative importance of each risk factor. After that, the assessments from FMEA team will be fused with a discounting-based combination rule to address the potential conflict. Moreover, to avoid the situation that different risk priorities of failure modes may have the same ranking based on classical risk priority number method, the gray relational projection method (GRPM) is adopted for ranking risk priorities of failure modes. Finally, an application of the improved FMEA model in sheet steel production process verifies the reliability and validity of the proposed method.     

Failure mode and effects analysis based on D numbers and TOPSIS

Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.     

FMEA下的超市食品HACCP计划制定与实现

结合故障模式与影响分析(FMEA)以及危害分析和关键控制点(HACCP)方法,可以分析超市食品供应中的潜在故障模式和决定给予风险优先级的关键控制点,从而可以产生一个HACCP计划.这一方法在一个超市产生猪肉的供应计划中成功地得到应用.     

An interval probability-based FMEA model for risk assessment: A real-world case

Failure mode and effect analysis (FMEA) is an effective quality tool to eliminate the risks and enhance the stability and safety in the fields of manufacturing and service industry. Nevertheless, the conventional FMEA has been criticized for its drawbacks in the evaluation process of risk factors or the determination of risk priority number (RPN), which may lead to inaccurate evaluation results. Therefore, in this paper, we develop a novel FMEA method based on rough set and interval probability theories. The rough set theory is adopted to manipulate the subjectivity and uncertainty of experts' assessment and convert the evaluation values of risk factors into interval numbers. Meanwhile, the interval exponential RPN (ERPN) is used to replace the traditional RPN due to its superior properties, eg, solving the problems of duplicate numbers and discontinuity of RPN values. Furthermore, an interval probability comparison method is proposed to rank the risk priority of each failure mode for avoiding the information loss in the calculation process of RPN. Finally, a real case study is presented, and the comparison analysis among different FMEA methods is conducted to demonstrate the reliability and effectiveness of the proposed FMEA method.     

Consensus building in linguistic failure mode and effect analysis: A perspective based on prospect theory

As one of many scientific and efficient risk assessment approaches, failure mode and effect analysis (FMEA) has been widely applied across various fields. There are two core issues in the FMEA approach: identifying the latent failure modes of the systems, products, processes and services and the risk assessment and the prioritization of those failure modes. Then, corrective measures must be taken in a timely and accessible manner to prevent the occurrence of failure modes with higher risk levels. In practice, several FMEA members from different fields are usually involved in the FMEA implementation process; the risk assessment information given by them may vary greatly. Therefore, it is necessary to integrate a consensus-building process into FMEA. Meanwhile, the psychological behaviours of FMEA members have had a great impact on the final prioritization of failure modes. Prospect theory is an effective approach for describing individual psychological behaviours. Therefore, this paper presents a novel linguistic FMEA approach to address the consensus issue from the perspective of prospect theory. In the proposed linguistic FMEA approach, a consensus measurement approach based on prospect theory is constructed. Then, a novel feedback adjustment mechanism is designed in which FMEA members can adjust not only their assessment information but also their reference points to achieve an acceptable consensus degree. Eventually, a practical application is used to show the validity and applicability of the proposed linguistic FMEA approach.     

Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty

Rotor blades are the major components of an aircraft turbine. Their reliability seriously affects the overall aircraft turbine security. Failure mode and effects analysis (FMEA), especially, the risk priority order of failure modes, is essential in the design process. The risk priority number (RPN) has been extensively used to determine the risk priority order of failure modes. When multiple experts give different risk evaluations to one failure mode, which may be imprecise and uncertain, the traditional RPN is not a sufficient tool for risk evaluation. In this paper, the modified Dempster–Shafer (D–S) is adopted to aggregate the different evaluation information by considering multiple experts’ evaluation opinions, failure modes and three risk factors respectively. A simplified discernment frame is proposed according to the practical application. Moreover, the mean value of the new RPN is used to determine the risk priority order of multiple failure modes. Finally, this method is used to deal with the risk priority evaluation of the failure modes of rotor blades of an aircraft turbine under multiple sources of different and uncertain evaluation information. The consequence of this method is rational and efficient.     

Failure mode and effects analysis by integrating Bayesian fuzzy assessment number and extended gray relational analysis‐technique for order preference by similarity to ideal solution method

Failure mode and effects analysis (FMEA) is a prospective risk assessment tool used to identify, assess, and eliminate potential failure modes (FMs) in various industries to improve security and reliability. However, the traditional FMEA method has been criticized for several shortcomings and even the improved FMEA methods based on predefined linguistic terms cannot meet the needs of FMEA team members' diversified opinion expressions. To solve these problems, a novel FMEA method is proposed by integrating Bayesian fuzzy assessment number (BFAN) and extended gray relational analysis‐technique for order preference by similarity to ideal solution (GRA‐TOPSIS) method. First, the BFANs are used to flexibly describe the risk evaluation results of the identified failure modes. Second, the Hausdorff distance between BFANs is calculated by using the probability density function (PDF). Finally, on the basis of the distance, the extended GRA‐TOPSIS method is applied to prioritize failure modes. A simulation study is presented to verify the effectiveness of the proposed approach in dealing with vague concepts and show its advantages over existing FMEA methods. Furthermore, a real case concerning the risk evaluation of aero‐engine turbine and compressor blades is provided to illustrate the practical application of the proposed method and particularly show the potential of using the BFANs in capturing FMEA team members' diverse opinions.     

基于Fuzzy-FMEA的AI绘画服务失效分析方法

目的 AI绘画是艺术对技术的借力,存在技术挟持艺术引发艺术与技术双重异化的风险,并表征于AI绘画服务端。为规避人工智能技术在绘画领域的不当应用,从服务失效角度对其进行风险分析十分必要。方法 针对传统FMEA存在忽视不确定性模糊评价等问题,提出一种引入模糊语言评价的Fuzzy-FMEA方法,用以识别AI绘画服务风险并确定整改优先级。首先设计体验实验,梳理AI绘画服务触点,记录受试者体验过程中的抱怨反应,计算抱怨指数,为后续Fuzzy-FMEA奠定基础;其次组建FMEA小组,建立模糊语言评价集合,对因人工智能技术应用不当影响智能体服务效能的服务触点进行失效分析,计算失效风险值,并对照第5版FMEA手册确定措施优先级。结论 结合实验数据输出风险预警报告和服务重塑方向,以具体案例验证了该方法的可行性与有效性。     

Applying UGF Concept to Enhance the Assessment Capability of FMEA

The purpose of this paper is to propose a modified version of Failure Mode and Effects Analysis (FMEA) to alleviate its drawbacks. FMEA is an important tool in risk evaluation and finding the priority of potential failure modes for corrective actions. In the proposed method, the Universal Generating Function (UGF) approach has been used to improve the assessment capability of the conventional Risk Priority Number (RPN) in ranking. The new method is named as URPN. It generates the most number of unique values in comparison with the previous methods and considers relative importance for the parameters while it is easy to compute. More unique numbers help to avoid from having the same priority level for different failure modes which represent various risk levels. A case study has been employed to demonstrate that the URPN not only can improve the shortcomings but also is able to provide accurate values for risk assessment. Copyright © 2015 John Wiley & Sons, Ltd.     

An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0-1 knapsack model

In this paper, a general framework for child injury prevention and a multi-objective, multi-dimensional mixed 0-1 knapsack model were developed to determine the optimal time to introduce preventive measures against child injuries. Furthermore, the model maximises the prevention of injuries with the highest risks for each age period by combining preventive measures and supervision as well as satisfying budget limits and supervision time constraints. The risk factors for each injury, variable, and time period were based on risk priority numbers (RPNs) obtained from failure mode and effects analysis (FMEA) methodology, and these risk factors were incorporated into the model as objective function parameters. A numerical experiment based on several different situations was conducted, revealing that the model provided optimal timing of preventive measures for child injuries based on variables considered.