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.     

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.     

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.     

Risk evaluation approaches in failure mode and effects analysis: A literature review

Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.     

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.     

Human factors risk assessment: An integrated method for improving safety in clinical use of medical devices

Medical devices play a critical role in care and treatment. The human-related failures can significantly affect the safety of patients in clinical use of medical devices. This study develops a comprehensive risk assessment model for identification and evaluation of failures which may occur in the clinical use of medical devices. First, the “Swiss cheese” model and SHEL model (the acronym of software, hardware, environment, and liveware) are integrated to comprehensively identify the potential human errors. Then, a new failure mode and effects analysis (FMEA) approach improved by rough set theory and grey relational analysis is developed to assess the risk of the identified failures. The proposed method integrates the strengths of the “Swiss cheese” and SHEL model in identifying human failures from both the vertical and horizontal perspectives of the system, and the advantages of the improved FMEA approach in flexibly manipulating vague information in risk evaluation without much priori information. Finally, the proposed method is applied in clinical use of respirator to verify its efficiency and effectiveness.     

A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the conventional RPN method has been considerably criticized for various reasons. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in risk evaluation process, this paper presents a novel approach for FMEA based on combination weighting and fuzzy VIKOR method. Integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach. The risk priorities of the identified failure modes are obtained through next steps based on fuzzy VIKOR method. To demonstrate its potential applications, the new fuzzy FMEA is used for analyzing the risk of general anesthesia process. Finally, a sensitivity analysis is carried out to verify the robustness of the risk ranking and a comparison analysis is conducted to show the advantages of the proposed FMEA approach.     

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.     

Fuzzy FMEA application to improve purchasing process in a public hospital

Failure mode and effects analysis (FMEA) is one of the well-known techniques of quality management that is used for continuous improvements in product or process designs. While applying this technique, determining the risk priority numbers, which indicate the levels of risks associated with potential problems, is of prime importance for the success of application. These numbers are generally attained from past experience and engineering judgments, and this way of risk assessment sometimes leads to inaccuracies and inconsistencies during priority numbering. Fuzzy logic approach is preferable in order to remove these deficiencies in assigning the risk priority numbers. In this study, a fuzzy-based FMEA is to be applied first time to improve the purchasing process of a public hospital. Results indicate that the application of fuzzy FMEA method can solve the problems that have arisen from conventional FMEA, and can efficiently discover the potential failure modes and effects. It can also provide the stability of process assurance.     

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.     

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.     

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.     

Risk analysis for the supplier selection problem using failure modes and effects analysis (FMEA)

While seeking for global suppliers is a general trend for lower cost and better quality, it is not trivial for a company to assess the corresponding risks in supplier selection. This paper proposes the supplier selection method that applies failures modes and effects analysis (FMEA) to assess the risks in the decision process. As each supplier is evaluated under the common multi-criteria framework, risks are viewed as the possible deviations from expected performance, and they are interpreted as failure modes in risk analysis. Following the concepts of FMEA, each failure mode is examined with respect to the possible causes and effects. This method generates two technical deliverables for supporting risk analysis. Firstly, the FMEA document is developed to support the team’s discussion of supplier risks and accumulate the risk knowledge within the company. Secondly, the ranking numbers based on FMEA (i.e., risk priority numbers) are utilized to evaluate a discount on a supplier’s performance according to their risk level. A real-case example about selecting methanol suppliers in the global market is used to demonstrate the proposed method for risk analysis in practice.     

Risk analysis for occupational safety and health in the textile industry: Integration of FMEA,FTA, and BIFPET methods

Identifying and managing health and safety risks that threaten personnel in production systems are vital for the continuity and success of organizations. Many tools are used to accurately analyze and assess risks. Failure mode and effect analysis (FMEA) is one of the most commonly used tools in different industries. However, the accuracy and reliability of FMEA method have been fairly criticized by many researchers in the field. In this study, an approach based on FMEA that integrates the advantages of the fault tree analysis (FTA) method and belief in fuzzy probability estimations of time (BIFPET) algorithm has been proposed in order to improve the performance of the FMEA method. In order to practically apply the proposed method to real life problems, it has been employed to analyze and assess the potential risks for a finishing process in the fabric dyeing department of a textile company. The performance of the proposed FMEA-FTA-BIFPET method has been compared to the results obtained by FMEA-FTA and FMEA-FTA-program evaluation and review technique (PERT) distribution integrated methods. The results of this study show that failure related to fabric trimming adjustment in the tenter has the highest risk priority number. The proposed approach can be used in various industry for risk analysis. In addition, results obtained by the study have indicated that the proposed approach can be implemented in practice to perform comprehensive risk assessment procedures as it reflects real-life dynamics to analyze and assess potential risk.     

Risk prioritization in failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is a powerful tool for identifying and assessing potential failures. The tool has become increasingly important in new product development, manufacture or engineering applications. Generally, risk assessment in FMEA is carried out by using risk priority numbers (RPNs) which can be determined by evaluating three factors: occurrence (O), severity (S) and detection (D). Due to the vagueness and uncertainty existing in the evaluating process, crisp numbers representing RPNs in the traditional FMEA might be improper or insufficient in contrast to fuzzy numbers. Currently, the fuzzy methods and linear programming method have been proposed as an effective solution for the calculations of fuzzy RPNs. However, considering the fact that fuzzy RPNs are determined on a multidimensional scale spanning O, S and D along with their interactions under a fuzzy environment, several gaps should be bridged in the evaluation, calculation, and ranking of fuzzy RPNs. First, decision makers tend to use multi-granularity linguistic term sets for expressing their assessments because of their different backgrounds and preferences. Second, numerical compensation may be existed among O, S and D that can derive different RPNs in the engineering applications. Third, the complete ranking results for fuzzy RPNs may be easily changed by the effects of uncertain factors. In this study, a fuzzy-RPNs-based method integrating weighted least square method, the method of imprecision and partial ranking method is proposed to generate more accurate fuzzy RPNs and ensure to be robust against the uncertainty. A design example of new horizontal directional drilling machine is used for illustrating the application of the proposed approach.     

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.     

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.     

Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure

Failure mode and effects analysis (FMEA), as a usefulness and powerful risk assessment tool, has been widely utilized in different industries for improving the safety and reliability of systems. However, the conventional risk priority number (RPN) method shows some important weaknesses when applied in actual situations. Moreover, FMEA is a group decision behavior and FMEA team members tend to use different linguistic term sets to express their judgments because of their different backgrounds and preferences, some of which may be imprecise, uncertain and incomplete. In this paper, we propose a new risk priority model using interval 2-tuple hybrid weighted distance (ITHWD) measure to solve the problems and improve the performance of the traditional FMEA. The new model can not only handle the uncertainty and diversity of FMEA team members’ assessment information but also consider the subjective and objective weights of risk factors in the risk ranking process. The model has exact characteristic and can avoid information distortion and loss in the linguistic information processing. Finally, a case study of blood transfusion is provided to demonstrate the effectiveness and benefits of the proposed approach.     

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.     

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。