基于SHERPA和FMEA的老年人医疗APP交互设计研究

目的 关爱老年人身体健康,设计适合老年人使用的医疗APP。方法 提出基于SHERPA和失效模式与效应分析（FMEA）的方法,先成立专家小组绘制医疗APP层次任务分析图,进行SHERPA分析,随后运用失效模式与效应分析找出APP潜在失效模式,计算风险优先数值,然后针对需要改善的失效模式进行设计改良,最终对改良后的APP进行结果验证。结论 将SHERPA和FMEA方法应用于老年人医疗APP,可以对任务的每一步骤进行错误预测,尽早发现问题,采取改善措施,体现了对老年人的人性化关怀,使他们在操作过程中获得良好的交互体验。     

基于经济损失的风险优先值排序方法研究及应用

针对传统失效模式和影响分析(FMEA)中风险优先等级(RPN)分析方法存在的离散性、重复性,以及灵敏度低、准确性差等缺陷,提出了一种基于经济损失的风险优先值排序方法——ERPN(risk priority number ranking method based on the economic losses).该方法采用定性与定量相结合的方式,首先确定某潜在失效情景的严重度,然后根据相关数据确定某初始事件发生概率以及此潜在失效情景所导致的经济损失,用两者的乘积作为ERPN值.该方法直接可观,可操作性强,具有较好的工程实用性.将其与基于反向鱼骨图的结构分解、基于情景的失效分析相结合,综合应用在自动包装生产线的送膜包覆装置的失效分析中,取得了很好的效果.     

模糊集理论与灰色关联理论的FMEA方法

在对传统FMEA研究的基础上,提出了基于模糊集理论和灰色关联理论的FMEA方法.结合模糊集理论建立评价失效模式的模糊语言术语集和模糊数,对各种失效模式进行评价,利用灰色关联理论计算各种失效模式的关联度,通过排序来确定失效模式的风险顺序.该方法克服了传统FMEA的缺陷,提高了FMEA应用的准确性和可靠性.     

基于FMEA和FTA的老年人汽车人机界面交互设计研究

目的 提升老年人汽车界面满意度,设计适合老年人使用的汽车界面.方法 提出基于失效模式与效应分析(FMEA)、故障树分析(FTA)的研究方法,首先成立会议小组进行SHERPA分析,绘制汽车界面层次任务分析图;随后运用FMEA找出汽车界面中的潜在失效模式,计算风险优先数值;接着选取风险优先数值高的失效模式,运用FTA寻找其发生的原因,对其以图形化表示;再对故障树进行定性定量分析,计算故障树的最小切集和失效事件发生概率,找到失效发生的核心因素;最后对失效事件进行改良并对改良后的结果进行验证.结论 以老年人汽车界面为例进行研究,该方法可以有效发现老年人操作汽车界面发生错误的原因,不仅可以帮助设计师找到改良的重点,还可以使老年人获得良好的驾驶体验.     

基于模糊集与灰色关联的改进FMEA方法

在对传统FMEA研究的基础上,提出了基于模糊集理论和灰色关联理论的改进FMEA.该方法首先结合模糊集理论建立评价失效模式的模糊语言术语集和对应的模糊数,并由FMEA小组对各种失效模式做出评价,然后利用灰色关联理论计算各种失效模式的关联度,通过排序来确定失效模式的风险顺序.     

基于改进FMEA的适老化智能药盒交互设计研究

目的 基于适老化智能药盒交互失效模式的数据,对智能药盒的交互模式进行优化设计,提升交互体验,有效降低交互差错率。方法 首先,成立改进FMEA专家小组进行系统性人因错误减少和预测方法分析（Systematic Human Error Reduction and Prediction Approach, SHERPA）;其次,构建智能药盒交互失误预防模型,然后对适老化智能药盒交互设计进行改进失效模式与效应分析（FailureModeand Effects Analysis, FMEA）;最后,针对需要改进的失效模式对智能药盒进行设计优化。结果 实验结果显示“实证对象”存在诸多交互失效模式,对相应交互失效模式进行优化,可有效提升老年人操作智能药盒的交互体验。结论 运用改进FMEA的方法,可以具体量化适老化智能药盒交互失效模式数据,对应失效模式数据的智能药盒交互优化设计,是可行有效的,能够有效降低老年人的用药风险,此方法可为产品交互设计研究提供新的方法路径。     

基于FMEA的老年人电子产品交互设计研究

目的设计适合老年人使用的电子产品。方法提出基于失效模式与效应分析的方法,成立FMEA专家小组进行讨论;对现有老年人电子产品进行潜在失效模式分析,计算风险优先数值(RPN);针对需要改进的失效模式进行合理的设计改良;针对改良后的产品进行设计评价。结论将失效模式与效应分析的方法应用于老年人电子产品设计,可以发现设计源头问题,尽早采取改进方案,从而使老年人在使用电子产品时,拥有一个良好的交互体验。     

产品技术进化潜力预测研究

  产品技术预测是企业长久保持竞争优势的有效方法,发明问题解决理论（TRIZ）的技术进化理论能够对技术预测提供理论指导．在介绍TRIZ的技术进化模式、技术进化路线的基础上,提出如何搜索产品技术进化潜力及构建进化潜力雷达图的方法,并对产品技术进化潜力预测技术作了系统的总结．基于产品技术进化潜力预测技术开发了专用软件——产品技术进化潜力预测系统（EPMS）,可帮助企业快速、准确地把握产品的技术走向,并应用软件EPMS对蝶阀密封技术的进化潜力进行了预测．     

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。     

基于FMEA的网购服务失误预防模型

从顾客的感知角度出发,拟采用失效模式与效应分析(FMEA)方法进行网购服务失误的预防。将容忍区域的概念引入到FMEA中,通过将服务质量感知差距模型与FMEA结合,建立网购服务失误预防模型。同时,对传统的风险优先数进行了改进,提出了适用于网购服务环境下的改进指数型风险优先数。改进后的风险优先数考虑了容忍区域,可以更加精确地描述潜在的服务失效模式,迅速辨别失效模式所处的区域。实证研究结果进一步表明了提出的网购服务失误预防模型的有效性。     

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.     

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 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.     

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.     

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 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.     

常闭阀罩生产过程质量控制研究

为了解决某公司常闭阀罩生产过程中质量不稳定、生产过程能力低等问题,将质量管理方法应用于常闭阀罩生产的管理过程。利用测量系统分析、过程能力分析、一元线性回归分析等方法进行产品质量的分析,并结合Minitab 软件里面的功能模块,精确计算过程能力指数,生成控制图,根据判定准则,给出判定结论。企业应用实例表明,该公司在实施了质量控制后,过程能力提高,废品率降低,有效地提高了产品质量。