Research on Reliability of Automatic Capping Device for Nuclear Power Plant

Automatic capping device is a complex and non-standard equipment, its reliability directly impacts the safety operation of Solid Waste Treatment System （TES） in nuclear power plant. In order to improve the reliability automatic capping device, the equipment function and machine structure are analyzed. And Failure Mode and Effects Analysis （FMEA） method is applied to systematically analyze all possible failure modes and their reliability. Through establishing the FMEA worksheet, all failure causes, failure effects and their severity are analyzed comprehensively. Base on these analyzing resuhs, it is easy to find out the product function design defects and weak links. Finally, through putting forward design prevention and improvement measures in design, the mission reliability of automatic capping device is improved, and most serious failure mode occurrence is avoided efficiently. Thus the safety operation of TES has been guaranteed in technology.     

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.     

Failure analysis requirements for nanoelectronics

Failure analysis (FA) plays a vital role in the development and manufacture of integrated circuits. However, instrumental limits are already threatening FA in the tenth-micron CMOS realm, and nanoelectronic devices will find key analytical tools two orders of magnitude removed in capability. This paper will introduce state-of-the-art microelectronic failure analysis processes, instrumentation, and principles. It will discuss the major limitations and future prospects determined from industry roadmaps. Specifically highlighted is the need for a fault isolation methodology for failure analysis of fully integrated nanoelectronics devices.     

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.     

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.     

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 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.     

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision‐making domain. Recent improvements on the FMEA have largely focussed on addressing the shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the risk priority number metrics, that is occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data are sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure of which the posterior function combines both prior functions elicited from experts and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability is demonstrated in the case study of thermal power plant equipment failures. Copyright © 2016 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 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.     

Noise-tolerant quantum MOS circuits using resonant tunneling devices

Resonant tunneling devices are promising candidates for comingling with traditional CMOS circuits, yielding better performance in terms of reduced silicon area, faster circuit speeds, lower power consumption, and improved circuit noise margin. These resonant tunneling devices have several intrinsic merits that include: high current density, low intrinsic capacitance, the negative differential resistance effect, and relative ease of fabrication. In this paper, we briefly describe some circuit configurations of Silicon quantum MOS logic family, with a special emphasis on noise-tolerant design that is now becoming an important constraint for robust and reliable operation of very deep submicron VLSI chips. More specifically, we discuss a novel strategy to incorporate quantum-tunneling devices into mainstream dynamic CMOS circuits with a view to improving the noise immunity of the latter. Dynamic CMOS circuits are rampantly used in modern high-performance VLSI chips achieving the best tradeoff between circuit speed, silicon area, and power consumption. However, they are inherently less noise-tolerant than their static CMOS counterparts. With the continuously deteriorating noise margins due to aggressive down scaling of the CMOS fabrication technologies, the performance overhead due to existing remedial noise-tolerant circuit techniques becomes prohibitively high. In this paper, we propose a novel method that utilizes the negative differential resistance property of quantum tunneling devices. The performance and noise immunity of the proposed circuits are evaluated through both analytical studies and SPICE simulations. We demonstrate that the noise tolerance of dynamic CMOS circuits can be greatly improved with very little degradation in circuit speed. The benefit of the proposed technique is evident even for currently available Silicon-based resonant tunneling devices with a relatively small peak-to-valley current ratio.     

Process failure modes and effects analysis (PFMEA): A structured approach for quality improvement in the metal forming industry

Failure modes and effects analysis (FMEA) is one potential tool with extended use in reliability engineering for the electrical and electronic components production field as well as in complicated assemblies (aerospace and automotive industries). The main purpose is to reveal system weaknesses and thereby minimize the risk of failure occurrence. The FMEA technique is used in the design stage of a system or product (DFMEA) as well as in the manufacturing process (PFMEA). Currently, the implementation of quality systems (such as ISO 9001, QS9000, TS 16949, etc.) requires the establishment of preventive procedures; therefore, the use of risk analysis methods, such as FMEA, is mandatory. This paper introduces the use of this technique in a critical process in the metal forming industry.     

Designing mechanical systems for optimum diagnosability

An analysis and modeling method of the diagnostic characteristics for electro-mechanical systems is presented. Diagnosability analysis is especially relevant given the complexities and functional interdependencies of modern-day systems, since improvements in diagnosability can lead to a reduction of a system’s life-cycle costs. Failure and diagnostic analysis leads to system diagnosability modeling with the failure modes and effects analysis (FMEA) and component-indication relationship analysis. Methods are then developed for translating the diagnosability model into mathematical methods for computing metrics such as distinguishability and No Fault Found. These methods involve the use of matrices to represent the failure and replacement characteristics of the system. Diagnosability metrics are extracted by matrix multiplication. These metrics are useful when comparing the diagnosability of proposed designs or predicting the life-cycle costs of fault isolation.     

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.     

An analytical framework for reliability growth of one-shot systems

In this paper, we introduce a new reliability growth methodology for one-shot systems that is applicable to the case where all corrective actions are implemented at the end of the current test phase. The methodology consists of four model equations for assessing: expected reliability, the expected number of failure modes observed in testing, the expected probability of discovering new failure modes, and the expected portion of system unreliability associated with repeat failure modes. These model equations provide an analytical framework for which reliability practitioners can estimate reliability improvement, address goodness-of-fit concerns, quantify programmatic risk, and assess reliability maturity of one-shot systems. A numerical example is given to illustrate the value and utility of the presented approach. This methodology is useful to program managers and reliability practitioners interested in applying the techniques above in their reliability growth program.     

Failure Mode Avoidance of Solid Rocket Motor Pressure Monitoring Joint Seals

The sealing joints used for pressure monitoring of solid propellant rocket motors (SRMs) of launch vehicles are very critical, as they are large in number, and leak through any of them is a single point failure mode. Identification of failure modes and its prevention is the key for reliable performance of an SRM. Failure modes are identified and the failure mechanisms of different seals in the pressure monitoring system studied through investigative tests with deliberately induced variations in the design parameters and nonconformance. Systematic analysis is carried out for the proposed designs through a failure mode effects analysis (FMEA), failure modes ranked in accordance with Risk Priority Number (RPN) and reliability of the joints worked out from the data. Design concerns are analyzed, alternate designs explored and innovative design solutions evolved. The effectiveness of the final design is brought out quantitatively by reduced RPN ratings and quantum jump in the reliability. Critical design, process and quality control parameters were identified, and procedures to ensure them evolved for failure mode avoidance.     

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.     

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.     

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.     

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.