Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment

Failure mode and effects analysis (FMEA) is a widely used risk assessment tool for defining, identifying, and eliminating potential failures or problems in products, process, designs, and services. In traditional FMEA, the risk priorities of failure modes are determined by using risk priority numbers (RPNs), which can be obtained by multiplying the scores of risk factors like occurrence (O), severity (S), and detection (D). However, the crisp RPN method has been criticized to have several deficiencies. In this paper, linguistic variables, expressed in trapezoidal or triangular fuzzy numbers, are used to assess the ratings and weights for the risk factors O, S, and D. For selecting the most serious failure modes, the extended VIKOR method is used to determine risk priorities of the failure modes that have been identified. As a result, a fuzzy FMEA based on fuzzy set theory and VIKOR method is proposed for prioritization of failure modes, specifically intended to address some limitations of the traditional FMEA. A case study, which assesses the risk of general anesthesia process, is presented to demonstrate the application of the proposed model under fuzzy environment.     

Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR

Enterprise architecture (EA) is an approach for managing all components of enterprise and relationships among them. By implementing EA, the organization will be threatened from different aspects. We used failure mode and effect analysis (FMEA) which is a powerful tool for evaluating EA risks. In traditional FMEA, risk priority number (RPN), has been calculated by multiplication of three criteria, severity, occurrence and detection. Because of some drawbacks of the traditional FMEA, this paper—instead of calculating RPN—prioritizes EA risk factors with fuzzy VIKOR. VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multi-criteria Optimization and Compromise Solution) is a multiple attribute decision making technique which aims to rank EA risk factors with respect to the criteria. As regards using linguistic variables, fuzzy approach is used to allow experts to use linguistic variables. The proposed method is used for evaluating twenty EA risk factors, which integrates knowledge and experience acquired from professional experts.     

A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis techniques due to its outstanding capabilities in identifying, assessing, and eliminating potential failure modes in a wide range of industrial applications. It provides a comprehensive view for investigating potential failures, causes, and effects in designs, products, and processes. However, traditional FMEA is extensively criticized for its defects in determining the criteria weights, identifying the risk priority of failure modes, and handling the uncertainty during the risk evaluation. To resolve these problems, this study proposes a novel fuzzy rough number extended multi-criteria group decision-making (FR-MCGDM) strategy to determine a more rational rank of failure modes by integrating the fuzzy rough number, AHP (analytic hierarchy process), and VIKOR (Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje). Above all, a fuzzy rough number is introduced to characterize experts’ judgment, aggregate group risk assessments, and tackle the uncertainty and subjectivity in the risk evaluation. Then a fuzzy rough number enhanced AHP is presented to determine the criteria weights. A fuzzy rough number enhanced VIKOR is proposed to rank the failure modes. A practical case study of the check valve is provided to validate the applicability of the proposed FMEA. Comparative studies demonstrate the efficacy of the proposed FR-MCGDM, with remarkable advantages in handling the uncertainty and subjectivity during failure modes evaluation.     

Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP

Failure mode and effects analysis (FMEA) is a widely used engineering technique for designing, identifying and eliminating known and/or potential failures, problems, errors and so on from system, design, process, and/or service before they reach the customer (Stamatis, 1995). In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O), and detectability (D) are evaluated and a risk priority number (RPN) is obtained by multiplying these factors. There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. In this study a fuzzy approach, allowing experts to use linguistic variables for determining S, O, and D, is considered for FMEA by applying fuzzy ‘technique for order preference by similarity to ideal solution’ (TOPSIS) integrated with fuzzy ‘analytical hierarchy process’ (AHP). The hypothetical case study demonstrated the applicability of the model in FMEA under fuzzy environment.     

Failure mode and effects analysis based on a novel fuzzy evidential method

Failure mode and effect analysis (FMEA) has been widely applied to examine potential failures in systems, designs, and products. The risk priority number (RPN) is the key criteria to determine the risk priorities of the failure modes. Traditionally, the determination of RPN is based on the risk factors like occurrence (O), severity (S) and detection (D), which require to be precisely evaluated. However, this method has many irrationalities and needs to be improved for more applications. To overcome the shortcomings of the traditional FMEA and better model and process uncertainties, we propose a FMEA model based on a novel fuzzy evidential method. The risks of the risk factors are evaluated by fuzzy membership degree. As a result, a comprehensive way to rank the risk of failure modes is proposed by fusing the feature information of O, S and D with Dempster–Shafer (D–S) evidence theory. The advantages of the proposed method are that it can not only cover the diversity and uncertainty of the risk assessment, but also improve the reliability of the RPN by data fusion. To validate the proposed method, a case study of a micro-electro-mechanical system (MEMS) is performed. The experimental results show that this method is reasonable and effective for real applications.     

结合OWA算子和模糊DEMATEL的风险评估方法

针对传统故障模式与影响分析(FMEA)方法在实际应用中的不足,提出一种基于有序加权平均(OWA)算子和决策试行与评价实验法(DEMATEL)的风险排序方法。FMEA专家对故障模式的3个风险因子给出模糊评价信息,应用OWA算子对评估信息进行集结,得到各故障原因对故障模式的影响强度。采用模糊DEMATEL法构建FMEA系统要素间的初始直接影响矩阵,经过运算可得综合影响矩阵,并计算各故障原因的原因度,据此进行产品或系统的失效风险评估。运用该方法对地铁车门系统的基础部件进行安全性分析,并将所得结果与传统RPN方法的结果做对比,验证了该方法的可行性和有效性。     

Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator

Failure mode and effects analysis (FMEA) has shown its effectiveness in examining potential failures in products, process, designs or services and has been extensively used for safety and reliability analysis in a wide range of industries. However, its approach to prioritise failure modes through a crisp risk priority number (RPN) has been criticised as having several shortcomings. The aim of this paper is to develop an efficient and comprehensive risk assessment methodology using intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED) operator to overcome the limitations and improve the effectiveness of the traditional FMEA. The diversified and uncertain assessments given by FMEA team members are treated as linguistic terms expressed in intuitionistic fuzzy numbers (IFNs). Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate the FMEA team members’ individual assessments into a group assessment. IFHWED operator is applied thereafter to the prioritisation and selection of failure modes. Particularly, both subjective and objective weights of risk factors are considered during the risk evaluation process. A numerical example for risk assessment is given to illustrate the proposed method finally.     

An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures

Identification and prioritization of failure modes in a system and planning for corrective actions are among the most important components of risk management in any organization. Meanwhile, conventional Failure Mode and Effects Analysis (FMEA) is one of the most commonly used methods for prioritization of the failures. Despite the widespread applications of this method in various industries, FMEA is associated with some shortcomings that can lead to unrealistic results. In this study, a proposed approach is presented in three phases to cover some of the shortcomings of the FMEA technique. In the first phase, FMEA is used to identify the failure modes and assign values to the Risk Priority Number (RPN) determinant factors. In the second phase, the Fuzzy Best-Worst Method (FBWM) based on the experts’ opinions is used to measure the weights of these factors. In the third phase, the outputs of the previous phases are used as a basis to prioritize the failures using the proposed Multi-Objective Optimization by Ratio Analysis based on the Z-number theory (Z-MOORA). In addition to assigning different weights to the RPN determinant factors and considering uncertainties of them, the Z-number theory is used in this approach to cover reliability in different failure modes. The proposed approach was implemented in the automotive spare parts industry, and the results indicate a full prioritization of the failures in comparison with other conventional methods such as FMEA and fuzzy MOORA.     

Evaluating the risk of failure using the fuzzy OWA and DEMATEL method

Most current risk assessment methods use the risk priority number (RPN) value to evaluate the risk of failure. However, traditional RPN methodology has been criticized to have several shortcomings. Therefore, an efficient, simplified algorithm to evaluate the orderings of risk for failure problems is proposed in this paper, which utilizes fuzzy ordered weighted averaging (OWA) and the decision making trial and evaluation laboratory (DEMATEL) approach to rank the risk of failure. The proposed approach resolves some of the shortcomings of the traditional RPN method. In numerical verification, a failure mode and effects analysis (FMEA) of the thin film transistor liquid crystal display (TFT-LCD) product is presented to further illustrate the proposed approach. The results show that the proposed approach can reduce duplicated RPN numbers and get a more accurate, reasonable risk assessment. As a result, the stability of product and process can be assured.     

Exploiting 2-tuple linguistic representational model for constructing HOQ-based failure modes and effects analysis

Failure modes and effects analysis (FMEA) is a widely recognized tool used to identify potential failure risks for improving the reliability and safety of new products. A major function of FMEA is to evaluate, analyze, and determine the risk priority number (RPN) of each potential failure mode based on three risk assessment indices: severity, occurrence, and detectability in order to eliminate the potential risk. Unlike in conventional practice, where these indices are measured using discrete ordinal scales, this study adopts a 2-tuple linguistic computational approach to treat the assessments of the three risk indices and develop an FMEA assessment model to overcome the drawbacks of conventional RPN calculation. The house of quality (HOQ), a central tool of quality function deployment, is adopted to determine the more reasonable evaluations of the occurrence and detectability indices. The three risk indices are considered with different weights in the proposed model to obtain RPNs. Compared to conventional and fuzzy FMEA, the proposed FMEA model is more useful for determining practical RPNs for risk analysis and management in new product development (NPD). An example is used to demonstrate the applicability of the proposed model.     

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.     

基于DEMATEL和VIKOR的服务外包风险评估

服务外包风险评估是企业供应商管理的重要依据。传统风险评估方法FMEA没有考虑风险模式间的相互影响关系,模糊DEMATEL方法用来分析风险模式的净影响度,进而修正其初始评估的严重度。针对传统FMEA方法将风险因子简单相乘的缺陷,采用模糊VIKOR方法对风险模式的严重度、发生频率和难检度进行综合评估。传统模糊VIKOR计算过程中需要进行模糊数的反模糊化处理,造成信息的损失,相对偏好关系分析引入模糊VIKOR计算解决这一问题。以某公司服务外包风险评估为例,验证了所提方法的有效性。     

Failure Mode and Effects Analysis Based on Z-numbers

The main objective of this paper is to propose a new method for failure mode and effects analysis (FMEA) based on Z-numbers. In the proposed method, firstly, Z-numbers are used to perform the valuations (Z-valuation) of the risk factors like occurrence (O), severity (S) and detection (D). Secondly, the Z-valuations of the risk factors are integrated by fuzzy weighted mean method. A new risk priority number named as ZRPN is calculated to prioritize failure modes based on a modified method of ranking fuzzy numbers. Finally, a case study for the rotor blades of an aircraft turbine is performed to demonstrate the feasibility of the proposed method.     

A model for failure mode and effects analysis based on intuitionistic fuzzy approach

Failure mode and effects analysis (FMEA) is one of the most powerful methods in the field of risk management and has been widely used for improving process reliability in manufacturing and service sector. High applicability of FMEA has contributed to its applications in many research domains and practical fields pertaining risk assessment and system safety enhancement. However, the method has also been criticized by experts due to several weaknesses and limitations. The current study proposed a novel model for failure mode and effects analysis based on intuitionistic fuzzy approach. This approach offers some advantages over earlier models as it accounts for degrees of uncertainty in relationships among various criteria or options, specifically when relations cannot be expressed in definite numbers. The proposed model provides a tool to evaluate the failure modes, while dealing with vague concepts and insufficient data. The proposed model was tested in a case study examining the failure modes for quality of internet banking services.     

Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach

Failure mode and effects analysis (FMEA) is a widely used engineering technique for identifying and eliminating known and potential failures from systems, designs, products, processes or services. However, the conventional risk priority number method has been extensively criticized in the literature for a lot of reasons such as ignoring relative importance of risk factors, questionable multiplication procedure, and imprecisely evaluation. In this article, a new FMEA model based on fuzzy digraph and matrix approach is developed to solve the problems and improve the effectiveness of the traditional FMEA. All the information about risk factors like occurrence (O), severity (S) and detection (D) and their relative weights are expressed in linguistic terms, represented by fuzzy numbers. By considering the risk factors and their relative importance, a risk factors fuzzy digraph is developed for the optimum representation of interrelations. Then, corresponding fuzzy risk matrixes are formed for all the identified failure modes in FMEA and risk priority indexes are computed for determining the risk priorities of the failure modes. Finally, a case study of steam valve system is included to illustrate the proposed fuzzy FMEA and the advantages are highlighted by comparing with the listed methods.     

Fuzzy modeling of system behavior for risk and reliability analysis

The main objective of the article is to permit the reliability analyst's/engineers/managers/practitioners to analyze the failure behavior of a system in a more consistent and logical manner. To this effect, the authors propose a methodological and structured framework, which makes use of both qualitative and quantitative techniques for risk and reliability analysis of the system. The framework has been applied to model and analyze a complex industrial system from a paper mill. In the quantitative framework, after developing the Petrinet model of the system, the fuzzy synthesis of failure and repair data (using fuzzy arithmetic operations) has been done. Various system parameters of managerial importance such as repair time, failure rate, mean time between failures, availability, and expected number of failures are computed to quantify the behavior in terms of fuzzy, crisp and defuzzified values. Further, to improve upon the reliability and maintainability characteristics of the system, in depth qualitative analysis of systems is carried out using failure mode and effect analysis (FMEA) by listing out all possible failure modes, their causes and effect on system performance. To address the limitations of traditional FMEA method based on risky priority number score, a risk ranking approach based on fuzzy and Grey relational analysis is proposed to prioritize failure causes.     

Fuzzy inference to risk assessment on nuclear engineering systems

This paper presents a nuclear case study, in which a fuzzy inference system (FIS) is used as alternative approach in risk analysis. The main objective of this study is to obtain an understanding of the aging process of an important nuclear power system and how it affects the overall plant safety. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base consists of a collection of fuzzy IF–THEN rules. The fuzzy inference engine uses these fuzzy IF–THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse based on fuzzy logic principles. The risk priority number (RPN), a traditional analysis parameter, was calculated and compared to fuzzy risk priority number (FRPN) using scores from expert opinion to probabilities of occurrence, severity and not detection. A standard four-loop pressurized water reactor (PWR) containment cooling system (CCS) was used as example case. The results demonstrated the potential of the inference system for subsiding the failure modes and effects analysis (FMEA) in aging studies.     

基于模糊逻辑的安全苛求系统失效模式与影响分析研究

针对传统RPN(风险优先数)方法在安全苛求系统FEMA(失效模式与影响分析)过程中的不足,提出一种基于FRPN(模糊风险优先数)的新方案,即运用模糊逻辑理论进行定量的FMEA。通过运用模糊加权几何平均计算失效模式的FRPN,据此对失效模式的风险程度排序,寻找影响系统安全性的主要因素,并将其作为改善对象。最后将该方法应用到高速铁路列控系统的FMEA过程中,结果表明,所提出的FRPN方法比传统RPN更加科学严谨,更能紧密联系应用环境,具有较高的科学性和实用价值。     

Failure mode and effects analysis using D numbers and grey relational projection method

Failure mode and effects analysis (FMEA) is a widely used risk assessment tool for defining, identifying and eliminating potential failures or problems in products, process, designs and services. Two critical issues of FMEA are the representation and handling of various types of assessments and the determination of risk priorities of failure modes. Many different approaches have been suggested to enhance the performance of traditional FMEA; however, deficiencies exist in these approaches. In this paper, based on a more effective representation of uncertain information, called D numbers, and an improved grey relational analysis method, grey relational projection (GRP), a new risk priority model is proposed for the risk evaluation in FMEA. In the proposed model, the assessment results of risk factors given by FMEA team members are expressed and modeled by D numbers. The GRP method is used to determine the risk priority order of the failure modes that have been identified. Finally, an illustrative case is provided to demonstrate the effectiveness and practicality of the proposed model.     

Risk analysis using FMEA: Fuzzy similarity value and possibility theory based approach

Fuzzy numerical technique for FMEA has been proposed to deal with the drawbacks of crisp FMEA and fuzzy rule based FMEA approaches. Fuzzy numerical approaches based on de-fuzzification also suffer from the drawback of providing arbitrary priority ranks of failure modes even when their membership functions overlap. To overcome this drawback we developed a new methodology integrating the concepts of similarity value measure of fuzzy numbers and possibility theory. Similarity value measure has been applied to group together failure modes having similar amount of risk value. The possibility theory has been used for checking for conformance guidelines. Two case studies have been shown to demonstrate the methodology thus developed. The proposed methodology is more robust in nature as it does not require arbitrary precise operations like de-fuzzification to prioritise the failure modes. Application of possibility theory is new to the domain of risk analysing using FMEA.