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.     

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。     

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.     

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.     

面向维修的机械系统可靠度预测与仿真研究

机电产品在服役期间因零件失效而产生故障,重组维修破坏了原有的系统可靠性模型,因而需要对设备可靠性问题重新进行研究和评价。基于机电系统中零件的失效时间分布密度函数,研究了在重组维护过程中机电系统服役期间零件年龄结构的分布规律,发展了机电系统可靠性数学模型。通过仿真研究,探讨了系统服役期间年龄结构、可靠度和失效率的发展规律,定量地研究了失效时间分布密度函数的参数对系统可靠度的影响。这对于评估机械系统的可靠性和全生命周期的失效率,制定合理的维修策略具有重要意义。     

EMS输送小车定位系统自动清洁装置的设计

目的解决EMS输送小车在运行中定位故障频发的问题。方法设计一种EMS输送小车定位系统自动清洁装置,该装置由定位条码清洁机构和读码头清洁机构等部分组成,通过对其关键技术的研究,进行定位条码自动清洁机构、电推杆、读码头自动清洁机构、自动清洁控制系统等的设计及相关控制参数的调整,实现定位系统及读码头的自动定时清洁。结果该自动清洁装置能够使EMS输送小车定位系统保持清洁,消除了定位故障造成的停机,停车定位准确率达到100%。结论实现了EMS输送小车系统无人值守运行,降低了EMS输送系统的运行成本,该装置还可应用于环形穿梭车、堆垛机、移动式翻箱机、条形码移动车等设备的清洁装置。     

30000m~3/h空分设备UPS电源失电时的处理

由于UPS电源突然失电造成DCS控制系统操作画面全部黑屏,无法对设备进行监控和操作,2套30000 m3/h空分设备被迫停车。分析造成UPS电源失电的原因,并叙述为保证停车后空分设备的安全,对空分设备各重要部位及相关阀门进行的监控和操作。     

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.     

失效模式影响分析在品牌安全管理中的应用

把FMEA用于品牌安全管理体系,要点在于区分品牌结构层次的安全和功能层次的安全,从而约定品牌的结构层次作为失效模式的观察对象,品牌功能层次为失效影响的观察对象。根据FMEA管理的思想,把由一个原因所产生的失效模式定义为一个失效模式,则该原因发生的概率、该原因导致失效模式发生的概率和失效模式的影响分别对应失效模式的发生度、检测度和严酷度。依此建立的FMEA管理表格和FMEA管理流程,可由企业的品牌管理人员实施,以对品牌安全进行管理。     

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.     

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.     

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.     

Study on reliability modelling of a hydraulic excavator system

The failure patterns of a well-known brand of a hydraulic excavator system used in different environments were modelled adequately with an NHPP (non-homogeneous Poisson process) having time-dependent log-linear peril rate functions. Using the fitted model, the reliability of the excavator system was estimated in different environments (cement plant, coal mine, iron ore mine, etc.). The system reliability was found to be very poor during the initial phase of operation and gradually improved with an increase in cumulative operating hours regardless of change in environment. Notwithstanding this general trend, the system reliability differed significantly in different environments for any given duration of operation and was particularly very poor in cement plants. With the help of the FMEA technique, high risk prone failure modes of the excavator system of the given model were identified and appropriate corrective measures were initiated. The failure patterns of the modified excavator system changed regardless of environment, so much so that an HPP (homogeneous Poisson process) model with constant peril rate could be fitted adequately to characterize the failure pattern of the system. The system reliability improved considerably in the initial phase of operation since start-up.     

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.     

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.     

FMEA在商品包装设计中的应用

介绍了FMEA的思想和应用特点,以啤酒包装为例分析了商品流通过程中包装所起的作用和可能出现的失效模式,并讨论了其影响;通过风险矩阵等方法对这些潜在的风险进行了分析,针对可能造成的影响给出了相应的措施,分析了其效果。以商品在流通中的物流过程为对象,提供了包装设计的具体步骤,对包装的绿色设计和最优应用提供了一条新颖的思路。     

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.     

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.     

Integrating failure analysis and risk analysis with quality assurance in the design phase of medical product development

Today, manufacturing operates as a global network, which has created more awareness of the quality of products and services. A systematic and rational way of managing quality assurance is currently lacking. This can cause serious problems in sectors such as the medical industry, as product failure may not only cause time delays, but also create risks for the health and safety of patients and users. This paper proposes a quality assurance approach that incorporates risk analysis (based on ISO14971) and failure analysis (based on FMEA) into the product design phase to assure product quality in the short term and facilitate global manufacturing practices in the long run. The proposed approach includes a Markov model to predict product failure from a customer perspective, which serves as a checkpoint for feedback to provide a basis for quality assurance. A medical equipment firm is used as a test-bed to illustrate how the proposed approach works and to verify its effectiveness.     

基于故障数据的设备运行可靠性分析与评估

基于故障数据,对设备运行可靠性进行了分析与评估。对某汽车制造企业的一台卧式加工中心的故障数据进行了统计与分析,形成观测样本,并拟合出了设备故障间隔时间的概率密度分布函数和累计分布函数曲线,从而推断得出其分布规律可能服从威布尔分布。然后通过对威布尔分布函数相关性进行检验,验证了该设备的故障间隔时间分布服从威布尔分布。最后根据统计结果计算得出了该设备的各项可靠性评估指标。