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 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 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.     

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.     

飞机发动机燃料计量系统故障分析的一种方法

随着产品成本和复杂性的提高，故障模式影响分析已成为复杂系统设计过程中不可或缺的部分。但其有效性多年来一直存在争议，其主要原因在于采用以经验为主的定性推理法，分析繁琐，工作量大，导致很难确定每种故障模式的故障影响。本文从BIT设计的角度，以功能角色模型理论为基础，导出在反馈系统中的推理规则和故障模式影响分析方法。最后以喷气式发动机燃料计量系统为例，阐述对该系统进行故障分析的一般步骤。研究表明，本文提出的功能角色模型理论可有效提高故障模式影响分析效率。     

Fuzzy FMEA in risk assessment for test and calibration laboratories

In this study, it is aimed to compare traditional and fuzzy FMEA in identifying areas that may pose risks and need improvement in Test and Calibration Laboratories. Within this scope, FMEA is used in ranking the possible risks. One hundred ninety-nine failures are detected in 91 inspections, carried out in the Test and Calibration Laboratories. Since FMEA uses experts’ evaluations, which are considered subjective, fuzzy logic is implemented to the approach where the evaluations are presented with linguistic variables. The comparison of FMEA and fuzzy FMEA showed that there exists a high correlation between these two analyses and the order of priority based on the Fuzzy Risk Priority Number calculation is overlapping with the Risk Priority Number sequence. Fuzzy FMEA can also be considered when the evaluations are not trustworthy or incomplete. Therefore, this study can be addressed as an example of how fuzzy implementation to FMEA substantially be used instead of traditional FMEA when there exist qualitative, subjective or incomplete evaluations, or in cases where traditional FMEA has troubles in practice.     

Risk Failure Deployment: A novel integrated tool to prioritize corrective actions in failure mode and effects analysis

In this paper, a novel integrated tool for failure mode and effects analysis (FMEA), opportunely named Risk Failure Deployment (RFD), which is able to evaluate the most critical failure modes and provide analyst with a practical and step-by-step guidance by selecting the most effective corrective actions for removal/mitigation process of root causes, is presented. Thanks to the modification of the framework of the Manufacturing cost deployment (MCD) and to its well-structured use of matrices, the novel RFD is able both to handle the dependencies and interactions between different and numerous failures and to evaluate the most critical ones on the basis of the risk priority number (RPN). Thereafter, the logical relationship between root causes and failure modes is assessed. Successively, the prioritization of corrective actions that are the most suitable for root causes is executed using not only the RPN but also other criteria, such as the economic aspect and the ease of implementation, that are unavoidable to execute a rational and effective selection of improvement activities. The effectiveness and usefulness in practice of the original tool for the prioritization of corrective actions to mitigate the risks due to failure modes collected during FMEA are presented in a case study.     

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.     

A quantitative method for Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) is commonly used for designing maintenance routines by analysing potential failures, predicting their effect and facilitating preventive action. It is used to make decisions on operational and capital expenditure. The literature has reported that despite its popularity, the FMEA method lacks transparency, repeatability and the ability to continuously improve maintenance routines. In this paper an enhancement to the FMEA method is proposed, which enables the probability of asset failure to be expressed as a function of explanatory variables, such as age, operating conditions or process measurements. The probability of failure and an estimate of the total costs can be used to determine maintenance routines. The procedure facilitates continuous improvement as the dataset builds up. The proposed method is illustrated through two datasets on failures. The first was based on an operating company exploiting a major gas field in the Netherlands. The second was retrieved from the public record and covers degradation occurrences of nuclear power plants in the United States.     

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.     

New Failure Mode and Effects Analysis: An Evidential Downscaling Method

Failure mode and effects analysis (FMEA) is an engineering and management technique, which is widely used to define, identify, and eliminate known or potential failures, problems, errors, and risk from the design, process, service, and so on. In a typical FMEA, the risk evaluation is determined by using the risk priority number (RPN), which is obtained by multiplying the scores of the occurrence, severity, and detection. However, because of the uncertainty in FMEA, the traditional RPN has been criticized because of several shortcomings. In this paper, an evidential downscaling method for risk evaluation in FMEA is proposed. In FMEA model, we utilize evidential reasoning approach to express the assessment from different experts. Multi‐expert assessments are transformed to a crisp value with weighted average method. Then, Euclidean distance from multi‐scale is applied to construct the basic belief assignments in Dempster–Shafer evidence theory application. According to the proposed method, the number of ratings is decreased from 10 to 3, and the frame of discernment is decreased from 2¹⁰ to 2³, which greatly decreases the computational complexity. Dempster's combination rule is utilized to aggregate the assessment of risk factors. We illustrate a numerical example and use the proposed method to deal with the risk priority evaluation in FMEA. The results and comparison show that the proposed method is more flexible and reasonable for real applications. Copyright © 2014 John Wiley & Sons, Ltd.     

Risk evaluation and mitigation of sustainable road freight transport operation: a case of trucking industry

Road freight transportation sustainability is gaining increasing importance due to an ever-increasing freight movement, globalisation and operational flexibility. The transport managers across the globe are finding it difficult to manage the increasing risks in its operation and implementation of risk-mitigation strategies under economical sustainability. However, very few literatures have examined the impact of sustainable risk management practices on road freight transportation. Our study addresses this important gap in the literature by proposing an integrated fuzzy failure mode and effects analysis (FMEA) approach in the selection of risk-mitigation strategy on the trucking industry. Our findings direct to the managers that risk-mitigation strategies must be selected considering the criticality of risks along with the limited budget. In addition, we find that considering subjective evaluations of expert’s judgment and cost benefit justification gives a convincing outcome while calculating of risk-mitigation number in FMEA approach. Proposed approach provides supportive guidelines to the manager to improve the decision-making process.     

Prioritising technical attributes in QFD under vague environment: a rough-grey relational analysis approach

This study aims at improving the effectiveness of Quality function deployment (QFD) in handling the vague, subjective and limited information. QFD has long been recognised as an efficient planning and problem-solving tool which can translate customer requirements (CRs) into the technical attributes of product or service. However, in the traditional QFD analysis, the vague and subjective information often lead to inaccurate priority. In order to solve this problem, a novel group decision approach for prioritising more rationally the technical attributes is proposed. Basically, two stages of analysis are described: the computation of CR importance and the prioritising the technical attributes with a hybrid approach based on a rough set theory (RST) and grey relational analysis (GRA). The approach integrates the strength of RST in handling vagueness with less priori information and the merit of GRA in structuring analytical framework and discovering necessary information of the data interactions. Finally, an application in industrial service design for compressor rotor is presented to demonstrate the potential of the approach.     

Failure mode and effect analysis using multi-linguistic terms and Dempster–Shafer evidence theory

Failure mode and effect analysis (FMEA) is a useful technique to identify and quantify potential failures. FMEA determines a potential failure mode by evaluating risk factors. In recent years, there are many works improving FMEA by allowing multiple experts to use linguistic term sets to evaluate risk factors. However, it is important to design a framework that can consider both the weight of risk factors and the weight of the experts. In addition, managing conflicts among experts is also an urgent problem to be addressed. In this paper, we proposed an FMEA model based on multi-granularity linguistic terms and the Dempster–Shafer evidence theory. On the other hand, the weights for both experts and risk factors are taken into consideration. The weights are computed objectively and subjectively to ensure the reasonability. Further, we apply our method to an emergency department case, which shows the effectiveness of the method.     

基于HACCP和FMEA的水产品冷链物流全流程优化

目的 解决水产品冷链物流损耗率高的问题,最大程度地提高水产品在冷链物流全流程中的质量安全。方法 采用危害分析与关键控制点（Hazard Analysis and Critical Control Point, HACCP）质量控制体系,详细解释水产品冷链物流的各个环节,并绘制其流程图,结合流程图分析各个环节的潜在危害,并通过潜在失效模式及后果分析（Failure Mode and Effects Analysis, FMEA）定量确定关键控制点,同时制定HACCP计划表。采用PDCA(Plan、Do、Check、Action)和SDCA(Standardization、Do、Check、Action)双循环优化方法分析流程存在的问题,并提出优化措施,绘制优化后水产品冷链物流的流程图。结果 确定水产品养殖捕捞、冷藏加工、冷冻贮存、冷冻运输、冷藏销售5个环节为关键控制点。结论 对关键控制点的潜在危害提出预防措施,并进行流程优化,尤其需要重点关注水产品的养殖捕捞和水产品冷藏销售环节,通过改善全流程的温度控制水平,加强对致病菌污染的监管,有效地防范并控制水产品冷链物流质量风险。     

Time-varying FMEA method based on interval-valued spherical fuzzy theory

This paper proposes a time-varying failure mode and effect analysis (FMEA) method based on interval-valued spherical fuzzy theory, which not only improves the limitations in evaluating, weighting, and ranking but also considers the effect of time change. The process of distinguishing time changes enables the FMEA to have dynamic recognition capability, enabling it to identify critical failure modes more accurately. The interval-valued spherical fuzzy theory is used to deal with the uncertainty of intuitionistic linguistic evaluations. The advantages of two traditional approaches are combined to improve the weight determined method. Risk factors are divided into subjective and objective types. In the subjective risk factors, which are severity (S) and detection (D), the consistency of judgment is used as the acceptance standard. In the objective risk factors, which are occurrence (O), the time-varying characteristics are considered. The occurrence in a certain period is expressed as the integral of failure intensity in the time period. Interval-valued spherical fuzzy exponential risk priority number is proposed as the criterion for measuring the priority of failure modes. The effectiveness of the proposed method is verified using an example of spindle.     

Fuzzy logic-based FMEA robust design: a quantitative approach for robustness against groupthink in group/team decision-making

Group/team decision-making is an integral part of almost all failure mode and effects analysis (FMEA) projects. A dysfunctional aspect of this decision-making fashion in fuzzy FMEA is that group/team members’ designs for membership functions and IF-THEN rules may be overshadowed by a member’s design. This problem is caused by groupthink, a pitfall known by the Organisational Behaviour science. This study aims to develop a fuzzy FMEA approach which is robust to the problem. We applied the Taguchi’s robust parameter design and investigated the effects of various control parameters namely Defuzzification, Aggregation, And and Implication operators for the fuzzy inference system (FIS). Our experiments illustrate that the control parameters, in the above-mentioned order, have the most effect on the signal-to-noise ratio (SNR). These factors’ optimal setting consists of the Centroid, Sum, Minimum and Minimum levels, respectively.     

Fuzzy assessment of FMEA for engine systems

When performing failure mode and effects analysis (FMEA) for quality assurance and reliability improvement, interdependencies among various failure modes with uncertain and imprecise information are very difficult to be incorporated for failure analysis. Consequently, the validity of the results may be questionable. This paper presents a fuzzy-logic-based method for FMEA to address this issue. A platform for a fuzzy expert assessment is integrated with the proposed system to overcome the potential difficulty in sharing information among experts from various disciplines. The FMEA of diesel engine's turbocharger system is presented to illustrate the feasibility of such techniques.     

A two-step methodology for CMOS VLSI reliability improvement: Step one

Reliability improvement of CMOS VLSI circuits depends on a thorough understanding of the technology, failure mechanisms, and resulting failure modes involved. Failure analysis has identified open circuits, short circuits and MOSFET degradations as the prominent failure modes. Classical methods of fault simulation and test generation are based on the gate level stuck-at fault model. This model has proved inadequate to model all realistic CMOS failure modes. An approach, which will complement available VLSI design packages, to aid reliability improvement and assurance of CMOS VLSI is outlined. A ‘two-step’ methodology is adopted. Step one, described in this paper, involves accurate circuit level fault simulation of CMOS cells used in a hierarchical design process. The simulation is achieved using SPICE and pre-SPICE insertion of faults (PSIF). PSIF is an additional module to SPICE that has been developed and is outlined in detail. Failure modes effects analysis (FMEA) is executed on the SPICE results and FMEA tables are generated. The second step of the methodology uses the FMEA tables to produce a knowledge base. Step two is essential when reliability studies of larger and VLSI circuits are required and will be the subject of a future paper. The knowledge base has the potential to generate fault trees, fault simulate and fault diagnose automatically.     

Automated multiple failure FMEA

Failure mode and effects analysis (FMEA) is typically performed by a team of engineers working together. In general, they will only consider single point failures in a system. Consideration of all possible combinations of failures is impractical for all but the simplest example systems. Even if the task of producing the FMEA report for the full multiple failure scenario were automated, it would still be impractical for the engineers to read, understand and act on all of the results.This paper shows how approximate failure rates for components can be used to select the most likely combinations of failures for automated investigation using simulation. The important information can be automatically identified from the resulting report, making it practical for engineers to study and act on the results. The strategy described in the paper has been applied to a range of electrical subsystems, and the results have confirmed that the strategy described here works well for realistically complex systems.