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.     

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.     

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.     

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.     

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.     

Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems

This paper presents a general methodology to improve risk assessment in the specific workshops of semiconductor manufacturers. We are concerned, in this case, with the problem of equipment failures and drifts. These failures are generally observed, with delay, during the product metrology phase. To improve the reactivity of the control system, we propose a predictive approach based on the Bayesian technique. Increased use of these techniques is the result of the advantages obtained. This approach allows early action to maintain, for example, the equipment before it can drift. Also, our contribution consists in proposing a generic model to predict the equipment health factor (EHF), which will define decision support strategies on preventive maintenance to avoid unscheduled equipment downtime. Following the proposed methodology, a data extraction and processing prototype is also designed to identify the real failure modes which will instantiate the Bayesian model. EHF results are decision support elements. They can be further used to improve production performance: reduced cycle time, improved yield and enhanced equipment effectiveness.     

A developed autonomous preventive maintenance programme using RCA and FMEA

Total productive maintenance (TPM) was developed in Japan in 1971 and has since been phased into many manufacturing firms to promote productivity and competitiveness. Autonomous preventive maintenance (APM) systems are very special. The fundamental pillar of TPM includes a series of important systematical first-line direct labour activities. The technical cost, human resources and management issues are all considered. Failure modes and effects analysis (FMEA) and root-cause analysis (RCA) are the most popular failure analytical methods, widely adopted over different industries. They are often used to examine the potential problems in the design and manufacturing phase, discovering possible failure causes before product design and manufacturing finalisation. This study integrates the RCA and FMEA techniques to establish an APM system that meets a company’s goal of reducing manufacturing costs and promoting employee and equipment productivity. The major contribution of this study is constructing potential equipment failure modes and their risk priority number through RCA and FMEA integration transformed into a selection of items and their APM maintenance frequencies. A strategy for deploying employee technical capability upgrade through effective training is developed. This study uses the S Company – a key manufacturer of semiconductor material – as a case study to verify the model’s applicability and suitability.     

A new risk prioritization model for failure mode and effects analysis

Failure modes and effects analysis is a framework that has been widely used to improve reliability by prioritizing failures modes using the so‐called risk priority number. However, the risk priority number has some problems frequently pointed out in literature, namely its non‐injectivity, non‐surjectivity, and the impossibility to give weights to risk variables. Despite these disadvantages, the risk priority number continues to be widely used due to its higher simplicity when compared with other alternatives found in literature. In this paper, we propose a novel risk prioritization model to overcome the major drawbacks of the risk priority number. The model contains 2 functions, the risk isosurface function that prioritizes 3 risk variables considering their order of importance in a given risk scenario, and the risk prioritization index function which prioritizes 3 risk variables considering their weights. The novelty of the proposed model is its injectivity, surjectivity, and ease of use in failure modes prioritization. The performance of the proposed model was analyzed using some examples typically used to discuss the conventional risk priority number shortcomings. The model was applied to a case study and its performance correlated with other risk prioritization models. Results show that the failure modes prioritization reached with the proposed model agrees with the expectations made for the risk scenario.     

A corrective maintenance scheme for engineering equipment

Corrective maintenance is a maintenance task performed to identify and rectify the cause failures for a failed system. The engineering equipment gets many components and failure modes, and its failure mechanism is very complicated. Failure of system-level might occur due to failure(s) of any subsystem/component. Thus, the symptom failure of equipment may be caused by multilevel causality of latent failures.This paper proposes a complete corrective maintenance scheme for engineering equipment. Firstly, the FMECA is extended to organize the numerous failure modes. Secondly, the failure propagation model (FPM) is presented to depict the cause-effect relationship between failures. Multiple FPMs will make up the failure propagation graph (FPG). For a specific symptom failure, the FPG is built by iteratively searching the cause failures with FPM. Moreover, when some failure in the FPG is newly ascertained to occur (or not), the FPG needs to be adjusted. The FPG updating process is proposed to accomplish the adjustment of FPG under newly ascertained failure. Then, the probability of the cause failures is calculated by the fault diagnosis process. Thirdly, the conventional corrective maintenance recommends that the failure with the largest probability should be ascertained firstly. However, the proposed approach considers not only the probability but also the failure detectability and severity. The term REN is introduced to measure the risk of the failure. Then, a binary decision tree is trained based on REN reduction to determine the failure ascertainment order. Finally, a case is presented to implement the proposed approach on the ram feed subsystem of a boring machine tool. The result proves the validity and practicability of the proposed method for corrective maintenance of engineering equipment.     

一种综合赋权的改进FMEA风险评估方法

针对传统故障模式和影响分析(failure mode and effects analysis, FMEA)方法中的未考虑风险因子权重以及风险因子权重难确定这一问题,提出一种综合赋权的改进FMEA风险评估方法。该方法首先通过FMEA团队明确评估对象和FMEA范围,然后列出所有潜在故障模式,对故障模式进行打分,得到所有的专家打分评估表,再通过语言变量转化为直觉模糊数。由层次分析法确定主观权重,由数据本身确定客观权重,使用直觉模糊混合加权算子(intuitionistic fuzzy hybrid weighted, IFHW)算子集结评价信息,得到所有的故障模式的得分函数,最后基于风险最大化选取每个故障模式的最大分数,进行排名,得到最终的故障模式风险顺序。通过对静电纺丝设备进行FMEA分析,并与其他方法进行比较,验证了所提方法的可行性。     

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.     

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.     

Application of RCM to a medium scale industry

The factors which are assuming considerable importance in cost effective decision making of operation of any industrial enterprise are in the order of significance liability, safety and environmental conditions. Hence, preventive maintenance (PM) optimisation is providing wide opportunities and challenges to everyone involved in all aspects of operation of industrial enterprise. Reliability centred maintenance (RCM) methodology offers the best available strategy for PM optimisation. It incorporates a new understanding of the ways in which equipment fails.In this paper, the concept of RCM has been applied to steel melting shop of a medium scale steel industry. By systematically applying the RCM methodology, failures, failure causes and effects on the system are analysed. To preserve the system function, PM categories are suggested for various failure modes in the components such as (1) time directed (2) condition directed (3) failure finding (4) run to failure. Features of predictive maintenance of a medium scale steel industry are deduced through this paper in a rather generalised form.     

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.     

An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory

mode and effects analysis (FMEA) is an effective tool to assess the risk of a system or process under uncertain environment. However, how to handle the uncertainty in the subjective assessment is an open issue. In this paper, a novel method to deal with the uncertainty coming from subjective assessments of FMEA experts is proposed in the framework of Dempster–Shafer evidence theory. First, the uncertain degree of the assessment is measured by the ambiguity measure. Then, the uncertainty is transformed to the reliability of each FMEA expert and the relative importance of each risk factor. After that, the assessments from FMEA team will be fused with a discounting-based combination rule to address the potential conflict. Moreover, to avoid the situation that different risk priorities of failure modes may have the same ranking based on classical risk priority number method, the gray relational projection method (GRPM) is adopted for ranking risk priorities of failure modes. Finally, an application of the improved FMEA model in sheet steel production process verifies the reliability and validity of the proposed method.     

FMEA下的超市食品HACCP计划制定与实现

结合故障模式与影响分析(FMEA)以及危害分析和关键控制点(HACCP)方法,可以分析超市食品供应中的潜在故障模式和决定给予风险优先级的关键控制点,从而可以产生一个HACCP计划.这一方法在一个超市产生猪肉的供应计划中成功地得到应用.     

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.     

Quality risk analysis in a cGMP environment: multiple models for comprehensive failure mode identification during the computer system lifecycle

Pharmaceutical quality systems use various inputs to ensure product quality and prevent failures that might have patient consequences. These inputs are generally data from failures that have already occurred, for example process deviations or customer complaints. Risk analysis techniques are well-established in certain other industries and have become of interest to pharmaceutical manufacturers because they allow potential quality failures to be predicted and mitigating action taken in advance of their occurring. Failure mode and effects analysis (FMEA) is one such technique, and in this study it was applied to implement a computerized manufacturing execution system in a pharmaceutical manufacturing environment. After introduction, the system was monitored to detect failures that did occur and these were analyzed to determine why the risk analysis method failed to predict them. Application of FMEA in other industries has identified weaknesses in predicting certain error types, specifically its dependence on other techniques to model risk situations and its poor analysis of non-hardware risks, such as human error, and this was confirmed in this study. Hierarchical holographic modeling (HHM), a technique for identifying risk scenarios in wide-scope analyses, was applied subsequently and identified additional potential failure modes. The technique for human error rate prediction (THERP) has previously been used for the quantitative analysis of human error risk and the event tree from this technique was adapted and identified further human error scenarios. These were input to the FMEA for prioritization and mitigation, thereby strengthening the risk analysis in terms of failure modes considered.     

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.