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.     

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.     

Site selection in waste management by the VIKOR method using linguistic assessment

Site selection is an important issue in municipal solid waste (MSW) management. Selection of the appropriate solid waste site is an extensive evaluation process that requires consideration of multiple alternative solutions and evaluation criteria. In reality, it is easier for decision makers to express their judgments on the alternatives by using linguistic terms, and there usually exists uncertain and incomplete assessment information. Moreover, decision makers may have different risk attitudes in the siting process because of their different backgrounds and personalities. Therefore, an attitudinal-based interval 2-tuple linguistic VIKOR (ITL-VIKOR) method is proposed in this paper to select the best disposal site for MSW. The feasibility and practicability of the proposed method are further demonstrated through an example of refuse-derived fuel (RDF) combustion plant location. Results show that the new approach is more suitable and effective to handle the MSW site selection problems by considering the decision maker's attitudinal character and incorporating the uncertain and incomplete assessment information.     

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.     

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.     

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 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.     

A perceptual computing-based method to prioritize failure modes in failure mode and effect analysis and its application to edible bird nest farming

A failure mode and effect analysis (FMEA) procedure that incorporates a novel Perceptual Computing (Per-C)–based Risk Priority Number (RPN) model is proposed in this paper. The proposed model considers linguistic uncertainties and vagueness of words, because it is more natural to use words, instead of numerals, for an FMEA user to express his/her knowledge when he/she provides an assessment. Therefore, it is important to consider the inherited uncertainties in words used by humans for assessment as an additional risk factor in the entire FMEA reasoning process. As such, we propose to use Per-C to analyze the uncertainties in words provided by different FMEA users. There are three potential sources of risks. Firstly, the risk factors of Severity (S), Occurrence (O), and Detection (D) are graded using words by each FMEA user, and indicated as interval type-2 fuzzy sets (IT2FSs). Secondly, the relative importance of S, O, and D are reflected by the weights given by each FMEA user in words, which are indicated as IT2FSs. Thirdly, the expertise level of each FMEA user is reflected by words, which are expressed as IT2FSs too. The proposed Per-C-RPN model allows these three sources of risks from each FMEA user to be considered and combined in terms of IT2FSs. A case study related to edible bird nest farming in Borneo Island is reported. The results indicate the effectiveness of the proposed model. In summary, this paper contributes to a new Per-C-RPN model that utilizes imprecise assessment grades pertaining to group decision making in FMEA.     

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.     

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.     

A model for evaluating computer network security systems with 2-tuple linguistic information

With respect to comprehensive evaluation model for computer network security with linguistic information and incomplete weight information, a new comprehensive evaluation model is proposed. In the method, the 2-tuple linguistic representation developed in recent years is used to aggregate the linguistic assessment information. In order to get the weight vector of the attribute, we establish an optimization model based on the basic ideal of traditional TOPSIS, by which the attribute weights can be determined. Then, the optimal alternative(s) is determined by calculating the shortest distance from the 2-tuple linguistic positive ideal solution (TLPIS) and on the other side the farthest distance of the 2-tuple linguistic negative ideal solution (TLNIS). The method has exact characteristics in linguistic information processing. It avoided information distortion and losing which occurred formerly in linguistic information processing. Finally, a numerical example of the evaluation of network security systems is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.     

A multi-granular linguistic model for management decision-making in performance appraisal

The performance appraisal is a relevant process to keep and improve the competitiveness of companies in nowadays. In spite of this relevance, the current performance appraisal models are not sufficiently well-defined either designed for the evaluation framework in which they are defined. This paper proposes a performance appraisal model where the assessments are modelled by means of linguistic information provided by different sets of reviewers in order to manage the uncertainty and subjectivity of such assessments. Therefore, the reviewers could express their assessments in different linguistic scales according to their knowledge about the evaluated employees, defining a multi-granular linguistic evaluation framework. Additionally, the proposed model will manage the multi-granular linguistic labels provided by appraisers in order to compute collective assessments about the employees that will be used by the management team to make the final decision about them.     

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.     

Some generalized aggregating operators with linguistic information and their application to multiple attribute group decision making

With respect to multiple attribute group decision making problems with linguistic information, some new decision analysis methods are proposed. Firstly, we develop three new aggregation operators: generalized 2-tuple weighted average (G-2TWA) operator, generalized 2-tuple ordered weighted average (G-2TOWA) operator and induced generalized 2-tuple ordered weighted average (IG-2TOWA) operator. Then, a method based on the IG-2TOWA and G-2TWA operators for multiple attribute group decision making is presented. In this approach, alternative appraisal values are calculated by the aggregation of 2-tuple linguistic information. Thus, the ranking of alternative or selection of the most desirable alternative(s) is obtained by the comparison of 2-tuple linguistic information. Finally, a numerical example is used to illustrate the applicability and effectiveness of the proposed method.     

Partitioned Bonferroni mean based on linguistic 2-tuple for dealing with multi-attribute group decision making

In this study, a multi-attribute group decision making (MAGDM) problem is investigated, in which decision makers provide their preferences over alternatives by using linguistic 2-tuple. In the process of decision making, we introduce the idea of a specific structure in the attribute set. We assume that attributes are partitioned into several classes and members of intra-partition are interrelated while no interrelationship exists among inter partition. We emphasize the importance of having an aggregation operator, to capture the expressed inter-relationship structure among the attributes, which we will refer to as partition Bonferroni mean (PBM). We also investigate the behavior of the proposed PBM operator. Further to aggregate the given linguistic information to get overall performance value of each alternative in MAGDM, we analyze PBM operator in linguistic 2-tuple environment and develop three new linguistic aggregation operators: 2-tuple linguistic PBM (2TLPBM), weighted 2-tuple linguistic PBM (W2TLPBM) and linguistic weighted 2-tuple linguistic PBM (LW-2TLPBM). Based on the idea that total linguistic deviation between individual decision maker's opinions and group opinion should be minimized, we develop an approach to determine weight of the decision makers. Finally, a practical example is presented to illustrate the proposed method and comparison analysis demonstrates applicability of the proposed method.     

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.     

基于广义优序法的语言型多属性群决策方法

针对具有语言评价信息的多属性群决策问题,提出基于广义优序法的语言型多属性群决策方法。该方法通过对传统优序法进行有效拓展,采用近年来最新发展的二元语义概念,将语言评价信息转化为二元语义形式的广义优序数,并在此基础上利用方案广义优序数的偏差最大化思想求解得到属性权重,最终确定最优方案。该方法对语言信息的处理较为精确,有效地避免了信息的丢失和扭曲。最后,通过对风险投资案例的分析结果表明了所提出方法的简洁性和有效性。     

基于前景理论的毕达哥拉斯二元语义PROMETHEE决策方法

针对属性值为毕达哥拉斯二元语义数(P2TLN)的多属性决策问题,考虑到决策者的有限理性行为,提出基于前景理论的偏好顺序结构排序法(PROMETHEE)的决策方法。首先,介绍毕达哥拉斯二元语义集的相关概念,并对现有P2TLN的距离进行改进,提出一种基于得分函数和精确函数的P2TLN距离,并证明其性质;其次,为体现决策者在比较决策信息时的风险偏好,利用前景价值函数构造P2TLN的优先函数,并以此对方案进行两两比较,从而计算各方案的净流量,进而对各方案进行排序。最后,通过物流公司的评估实例说明所提方法的可行性和有效性。     

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.     

区间二元语义Bonferroni集成算子及其决策应用

区间二元语义值是一种常用的不确定环境下决策信息表达形式。考虑到决策信息的交叉影响作用,定义了区间二元语义值的Bonferroni平均算子以及相应的加权形式,在此基础上,给出了组合形式的区间二元语义值的加权Bonferroni平均算子的概念,并研究了算子的幂等性、单调性等数学性质,给出了基于C-I2TLWBA算子区间二元语义值的集成模型和决策应用。实例表明了该模型具有较好的有效性。