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 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.     

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 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.     

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 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.     

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.     

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.     

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 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.     

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.     

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.     

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.     

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.     

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.     

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 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。