Applying UGF Concept to Enhance the Assessment Capability of FMEA

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

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

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

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

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

Utility Priority Number Evaluation for FMEA

Traditionally, decisions on how to improve an operation are based on risk priority number (RPN) in the failure mode and effects analysis (FMEA). Many scholars questioned the RPN method and proposed some new methods to improve the decision process, but these methods are only measuring from the risks viewpoint while ignoring the importance of corrective actions. The corrective actions may be interdependent; hence, if the implementation of corrective actions is in proper order, selection may maximize the improvement effect, bring favorable results in the shortest times, and provide the lowest cost. This study aims to evaluate the structure of hierarchy and interdependence of corrective action by interpretive structural model (ISM), then to calculate the weight of a corrective action through the analytic network process (ANP), then to combine the utility of corrective actions and make a decision on improvement priority order of FMEA by utility priority number (UPN). Finally, it verifies the feasibility and effectiveness of this method by application to a case study. An erratum to this article can be found at     

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

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

Failure mode and effect analysis: an application in reducing risk in blood transfusion

BACKGROUND: In February 2001 Good Samaritan Hospital in Dayton, Ohio, conducted a Failure Mode and Effect Analysis (FMEA) on the blood transfusion process to reduce the risk of problems inherent in the procedure. DEVELOPING THE FMEA: The major steps of the analysis were to identify problems (failure modes), define their causes, and surmise the effects if failures occurred. Numerical scores were assigned for the likelihood of failure occurrence, the severity of the effects, and the possibility that the failure would escape detection. These scores were multiplied and reported as a risk priority number (RPN) for each failure mode. Solutions (process redesign actions) and monitoring plans (design validation) were developed to address the failure modes with the highest RPNs. PRESENTING THE FMEA: In March 2001 the FMEA document was presented to the Safety Board, which approved design changes such as use of a blood barrier system that restricts access to the blood until a patient-specific code is dialed. RESULTS: Measures were developed to analyze results, and rapid-cycle Plan-Do-Study-Act methodology was used to test and document redesign changes; most became the standard operating procedure. The new process accomplished its purpose of preventing serious, avoidable errors. No outcome errors occurred from March 2001 through June 2001 or in the 8 months following housewide implementation on June 18, 2001. DISCUSSION: FMEA was a valuable tool in error-trapping the blood transfusion process. Yet the FMEA process was time-consuming, tedious, and difficult and should be reserved for an organization's highest-priority processes.     

A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis

Failure mode and effect analysis (FMEA) is a powerful tool for defining, identifying, and eliminating potential failures from the system, design, process, or service before they reach the customer. Since its appearance, FMEA has been extensively used in a wide range of industries. However, the conventional risk priority number (RPN) method has been criticized for having a number of drawbacks. In addition, FMEA is a group decision behavior and generally performed by a cross‐functional team. Multiple experts tend to express their judgments on the failure modes by using multigranularity linguistic term sets, and there usually exists uncertain and incomplete assessment information. In this paper, we present a novel FMEA approach combining interval 2‐tuple linguistic variables with gray relational analysis to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. An empirical example of a C‐arm X‐ray machine is given to illustrate the potential applications and benefits of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Modified failure mode and effects analysis using approximate reasoning

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

Using Fuzzy Cost‐Based FMEA,GRA and Profitability Theory for Minimizing Failures at a Healthcare Diagnosis Service

This paper proposes an integrated approach to identify, evaluate and improve the potential failures in a service setting. This integrated approach combines Fuzzy cost‐based service‐specific FMEA (FCS‐FMEA), Grey Relational Analysis (GRA) and profitability theory for better prioritization of the service failures by considering cost as an important issue and using the profitability theory in a way that the corrective actions costs are taken into account. Considering profitability with FCS‐FMEA and GRA reduces the losses caused by failure occurrence. Besides, a maximization linear mathematical problem is used to select the best mix of failures to be repaired. We apply our approach to an academic example concerning the potential failures diagnosis of the Internal Medicine service of a hospital located in Seoul, Korea. We applied our approach and solved the associated maximization problem by a commercial solver, producing an optimal solution which indicates the most convenient mix of failures to be repaired by considering available budget. Copyright © 2013 John Wiley & Sons, Ltd.     

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

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

Enhancing FMEA assessment by integrating grey relational analysis and the decision making trial and evaluation laboratory approach

Failure modes and effects analysis (FMEA) is used widely to improve product quality and system reliability, employing a risk priority number (RPN) to assess the influence of failures. The RPN is a product of three indicators—severity (S), occurrence (O), and detection (D)—on a numerical scale from 1 to 10. However, the traditional RPN method has been criticized for its four chief shortcomings: its (1) high duplication rate; (2) assumption of equal importance of S, O, and D; (3) not following the ordered weighted rule; and (4) failure to consider the direct and indirect relationships between failure modes (FMs) and causes of failure (CFs). To resolve these drawbacks, we propose a novel approach, integrating grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) method, to rank the risk of failure, wherein the GRA is used to modify RPN values to lower duplications and the ordered weighted rule is followed; then, the DEMATEL method is applied to examine the direct and indirect relationships between FMs and CFs, giving higher priority when a single CF causes FMs to occur multiple times. Finally, an actual case of the TFT-LCD cell process is presented to verify the effectiveness of our method compared with other methods in providing decision-makers more reasonable reference information.     

An alternative evaluation of FMEA: Fuzzy ART algorithm

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

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

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

Failure mode and effects analysis based on D numbers and TOPSIS

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

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

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

An Evidential Failure Mode and Effects Analysis Using Linguistic Terms

Failure mode and effects analysis (FMEA) has been extensively used in reliability engineering domain. Risk priority number (RPN), defined as the product of occurrence (O), severity (S), and detection (D) of a failure, is the most important measure used in FMEA for prioritizing risk. In this paper, a new evidential FMEA using linguistic term is presented. First, the linguistic terms have been applied in the form of assessment distribution, which enables the experts to express their assessments in a more realistic way and hence improving the applicability of the FMEA. Second, a novel method to transform the experts' linguistic judgments into basic probability assignments (BPAs) is proposed. Third, the flexibility of assigning a weight to each criterion in the FMEA provides a means of specifically identifying weak areas in the system/component studied. At the same time, the weights can be utilized as the discounting coefficients to address the problem existing in conflicting evidence combination. An example is illustrated to show the practical application of the proposed FMEA methodology in engineering. Copyright © 2016 John Wiley & Sons, Ltd.     

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

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

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

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

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

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

基于改进的TOPSIS法与RSR法的质量改善项目综合评估

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