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Journal articles on the topic 'Failure mode and effects analysis (FMEA)'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Alruqi, Mansoor, Martin Baumers, David Branson, and Robert Farndon. "A Structured Approach for Synchronising the Applications of Failure Mode and Effects Analysis." Management Systems in Production Engineering 29, no. 3 (June 24, 2021): 165–77. http://dx.doi.org/10.2478/mspe-2021-0021.

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Abstract Failure Mode and Effects Analysis (FMEA) is a systematic approach for evaluating the potential failure modes in a system, and is mainly employed in three distinct tasks labelled: (1) Functional FMEA – evaluating those failures associated with product functional definition, (2) Design FMEA – analysing those failures associated with design definition and (3) Process FMEA – assessing potential failures in manufacturing and assembly processes. The literature review has shown limited works on the field of synchronising these different tasks into a working model. To address this gap, this research developed a framework for integrating these tasks of FMEAs, and then qualitatively validating the proposed framework. This research adopted a semi-structured questionnaire to collect experts’ feedback and validate the proposed framework. The t-test was then employed to evaluate the collected feedback. The findings highlight that the proposed framework is applicable and could facilitate the synchronisation of the different tasks of FMEA. This research presents a methodological approach for executing and synchronising FMEAs. Therefore, the proposed framework is practically relevant as an aid for the practitioners in catching the cascading failures and reducing the relevant impact.
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BOUTI, ABDELKADER, and DAOUD AIT KADI. "A STATE-OF-THE-ART REVIEW OF FMEA/FMECA." International Journal of Reliability, Quality and Safety Engineering 01, no. 04 (December 1994): 515–43. http://dx.doi.org/10.1142/s0218539394000362.

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The Failure Mode and Effects Analysis (FMEA) documents single failures of a system, by identifying the failure modes, and the causes and effects of each potential failure mode on system service and defining appropriate detection procedures and corrective actions. When extended by Criticality Analysis procedure (CA) for failure modes classification, it is known as Failure Mode Effects and Criticality Analysis (FMECA). The present paper presents a literature review of FME(C)A, covering the following aspects: description and review of the basic principles of FME(C)A, types, enhancement of the method, automation and available computer codes, combination with other techniques and specific applications. We conclude with a discussion of various issues raised as a result of the review.
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Krouwer, Jan S. "An Improved Failure Mode Effects Analysis for Hospitals." Archives of Pathology & Laboratory Medicine 128, no. 6 (June 1, 2004): 663–67. http://dx.doi.org/10.5858/2004-128-663-aifmea.

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Abstract Objective.—To review the Failure Mode Effects Analysis (FMEA) process recommended by the Joint Commission on Accreditation of Health Organizations and to review alternatives. This reliability engineering tool may be unfamiliar to hospital personnel. Data Sources.—Joint Commission on Accreditation of Health Organizations recommendations, Mil-Std-1629A, and other articles about FMEA were used. Study Selection.—The articles were selected by a literature search that included Web site–accessible material. Data Extraction.—All articles found were used. Data Synthesis.—The results are based on the articles cited and the author's experience in conducting FMEAs in the medical diagnostics industry. Conclusions.—Fault trees and a list of quality system essentials are recommended additions to the FMEA process to help identify failure mode effects and causes. Neglecting mitigations for failure modes that have never occurred is a possible danger when too much emphasis is placed on improving risk priority numbers. A modified Pareto, not based on the risk priority number, is recommended when there are qualitatively different failure mode effects with different severities. Performing a FMEA that both meets accreditation requirements and reduces the risk of medical errors is an attainable goal, but it may require a different focus.
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Price, C. J., J. E. Hunt, M. H. Lee, and A. R. T. Ormsby. "A Model-Based Approach to the Automation of Failure Mode Effects Analysis for Design." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 206, no. 4 (October 1992): 285–91. http://dx.doi.org/10.1243/pime_proc_1992_206_189_02.

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This paper describes the application of model-based technology in the area of failure mode effects analysis (FMEA). FMEA involves the repetitive application of engineering expertise, and so would seem to be a promising target for automation through knowledge-based systems (KBS) technology. However, in order to decide what would be the effect of a failure in a sub-system, it is necessary to reason about the structure of the sub-system being investigated and to be able to represent and reason about different facets of the way in which the sub-system works. The difficulty of automating this analysis of failure effects for new designs has meant that, in general, automation of the FMEA task has concentrated on only the clerical aspects of FMEA—helping the human expert to keep track of which possible failures have been analysed. The work described in this paper automates the analysis phase of the FMEA process. This should enable the development of computerized aids for the FMEA engineer which will eliminate much of the tediousness of the task.
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Roy, Soumen Kumar, A. K. Sarkar, and Biswajit Mahanty. "Fuzzy risk assessment for electro-optical target tracker." International Journal of Quality & Reliability Management 33, no. 6 (June 6, 2016): 830–51. http://dx.doi.org/10.1108/ijqrm-03-2015-0034.

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Purpose – The purpose of this paper is to evolve a guideline for scientists and development engineers to the failure behavior of electro-optical target tracker system (EOTTS) using fuzzy methodology leading to success of short-range homing guided missile (SRHGM) in which this critical subsystems is exploited. Design/methodology/approach – Technology index (TI) and fuzzy failure mode effect analysis (FMEA) are used to build an integrated framework to facilitate the system technology assessment and failure modes. Failure mode analysis is carried out for the system using data gathered from technical experts involved in design and realization of the EOTTS. In order to circumvent the limitations of the traditional failure mode effects and criticality analysis (FMECA), fuzzy FMCEA is adopted for the prioritization of the risks. FMEA parameters – severity, occurrence and detection are fuzzifed with suitable membership functions. These membership functions are used to define failure modes. Open source linear programming solver is used to solve linear equations. Findings – It is found that EOTTS has the highest TI among the major technologies used in the SRHGM. Fuzzy risk priority numbers (FRPN) for all important failure modes of the EOTTS are calculated and the failure modes are ranked to arrive at important monitoring points during design and development of the weapon system. Originality/value – This paper integrates the use of TI, fuzzy logic and experts’ database with FMEA toward assisting the scientists and engineers while conducting failure mode and effect analysis to prioritize failures toward taking corrective measure during the design and development of EOTTS.
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Haktanır, Elif, and Cengiz Kahraman. "Interval-valued neutrosophic failure mode and effect analysis." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6591–601. http://dx.doi.org/10.3233/jifs-189121.

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Failure mode and effects analysis (FMEA) is a structured approach for discovering possible failures that may occur in the design of a product or process. Since classical FMEA is not sufficient to represent the vagueness and impreciseness in human decisions and evaluations, many extensions of ordinary fuzzy sets such as hesitant fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, spherical fuzzy sets, and picture fuzzy sets. Classical FMEA has been handled to capture the uncertainty through these extensions. Neutrosophic sets is a different extension from the others handling the uncertainty parameters independently. A novel interval-valued neutrosophic FMEA method is developed in this study. The proposed method is presented in several steps with its application to an automotive company in order to prioritize the potential causes of failures during the design process by considering multi-experts’ evaluations.
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Pino, Felicity A., Darcy K. Weidemann, Lisa L. Schroeder, Damon B. Pabst, and Audrey R. Kennedy. "Failure mode and effects analysis to reduce risk of heparin use." American Journal of Health-System Pharmacy 76, no. 23 (October 17, 2019): 1972–79. http://dx.doi.org/10.1093/ajhp/zxz229.

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Abstract Purpose Failure mode and effects analysis (FMEA) was used to identify safety risks of unfractionated heparin (UFH) use and to develop and implement countermeasures to improve safety. Methods FMEA was used to analyze the transportation, preparation, dispensation, administration, therapeutic monitoring, and disposal of UFH in a tertiary care, freestanding pediatric hospital. The FMEA was conducted in a stepwise fashion. First, frontline staff mapped the different steps within the UFH use process. Next, key stakeholders identified potential failures of each process step. Finally, using calibrated scales, the stakeholders ranked the likelihood of occurrence, severity, and detectability for each potential failure’s cause. The rankings were used to prioritize high-risk areas on which to focus efforts for improvement countermeasures. Results The analysis revealed 233 potential failures and 737 unique potential causes. After ranking of all identified potential causes, 45 were deemed high scoring. Those 45 causes were further refined into 13 underlying contributing causes. To address the contributing causes, selected team members developed 22 countermeasures. The FMEA showed that implementation of the countermeasures reduced the level of mathematical risk. Conclusion FMEA was helpful in identifying, ranking, and prioritizing medication risks in the UFH use process. Twenty-two countermeasures were developed to reduce potential for error in the riskiest steps of the process.
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Shi, Jun Li, Hong Wei Qu, Ya Jun Wang, Yan Qiu Liu, Ke Xin Wang, and Jia Lin Li. "Application of Improved Failure Modes and Effects Analysis in Product Reliability Analysis." Advanced Materials Research 926-930 (May 2014): 3438–41. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3438.

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On the analysis of the disadvantage of traditional Failure Mode and Effect Analysis (FMEA), an improved FMEA based on fuzzy set theory and Analytical Hierarchy Process (AHP) is proposed. In this method, the fuzzy language set and the fuzzy number of Severity (S), Occurrence (O) and Detection (D) are firstly set up, then failure modes are evaluated, and finally the weights of S, O and D are determined by AHP. So, risk priorities of the failure modes can be determined by calculating the modified Risk Priority Number (RPN). Improved FMEA could provide a method for the company to evaluate the reliability analysis of products.
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Dong, Yu Liang, Ye Su, and Cheng Bing He. "Wind Turbine Risk Assessment Using Modified Failure Mode and Effects Analysis." Applied Mechanics and Materials 385-386 (August 2013): 1141–44. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1141.

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Aiming at the problem that the failure history data available from wind turbine are scarce and often accompanied with a high degree of uncertainty, the risk assessment using traditional failure mode and effects anaysis (FMEA) may not be well-suited. A wind turbine risk assessment method based on modified FMEA is proposed. The method introduces grey theory into traditional FMEA and use the degree of relation to rank risk level. The method is then used to assess the risk of a 1.5MW wind turbine. It is proved that this method can rank the risks of main failure modes.The assessment results can be used as a support for risk based maintenance decisions.
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Zheng, Haixia, and Yongchuan Tang. "Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis." Entropy 22, no. 3 (February 28, 2020): 280. http://dx.doi.org/10.3390/e22030280.

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Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model.
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Zhu, Jianghong, Bin Shuai, Rui Wang, and Kwai-Sang Chin. "Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method." Mathematics 7, no. 6 (June 12, 2019): 536. http://dx.doi.org/10.3390/math7060536.

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As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This study aims at presenting a comprehensive FMEA model that could efficiently handle the preference interdependence and psychological behavior of experts in the process of failure modes ranking. In this model, a linguistic variable expressed by the interval-valued Pythagorean fuzzy number (IVPFN) is utilized by experts to provide preference information with regard to failure modes’ evaluation and risk factors’ weight. Then, to depict the interdependent relationships between experts’ preferences, the Bonferroni mean operator is extended to IVPFN to aggregate the experts’ preference. Subsequently, an extended TODIM approach in which the dominance degree of failure modes is calculated by grey relational analysis is utilized to determine the risk priority of failure modes. Finally, a practical example concerning the risk assessment of a nuclear reheat valve system is provided to demonstrate the effectiveness and feasibility of the presented method. In addition, a sensitivity analysis and comparison analysis are conducted, and the results show that the preference interdependence and psychological behavior of experts have an important effect on the risk priority of failure modes.
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Shebl, Nada, Bryony Franklin, Nick Barber, Susan Burnett, and Anam Parand. "Failure Mode and Effects Analysis: Views of Hospital Staff in the UK." Journal of Health Services Research & Policy 17, no. 1 (January 2012): 37–43. http://dx.doi.org/10.1258/jhsrp.2011.011031.

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Objective To explore health care professionals' experiences and perceptions of Failure Mode and Effects Analysis (FMEA), a team-based, prospective risk analysis technique. Methods Semi-structured interviews were conducted with 21 operational leads (20 pharmacists, one nurse) in medicines management teams of hospitals participating in a national quality improvement programme. Interviews were transcribed, coded and emergent themes identified using framework analysis. Results Themes identified included perceptions and experiences of participants with FMEA, validity and reliability issues, and FMEA's use in practice. FMEA was considered to be a structured but subjective process that helps health care professionals get together to identify high risk areas of care. Both positive and negative opinions were expressed, with the majority of interviewees expressing positive views towards FMEA in relation to its structured nature and the use of a multidisciplinary team. Other participants criticised FMEA for being subjective and lacking validity. Most likely to restrict its widespread use were its time consuming nature and its perceived lack of validity and reliability. Conclusion FMEA is a subjective but systematic tool that helps identify high risk areas, but its time consuming nature, difficulty with the scores and perceived lack of validity and reliability may limit its widespread use.
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Molnár, Vince, and István Majzik. "Model Checking-based Software-FMEA: Assessment of Fault Tolerance and Error Detection Mechanisms." Periodica Polytechnica Electrical Engineering and Computer Science 61, no. 2 (April 24, 2017): 132. http://dx.doi.org/10.3311/ppee.9755.

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Failure Mode and Effects Analysis (FMEA) is a systematic technique to explore the possible failure modes of individual components or subsystems and determine their potential effects at the system level. Applications of FMEA are common in case of hardware and communication failures, but analyzing software failures (SW-FMEA) poses a number of challenges. Failures may originate in permanent software faults commonly called bugs, and their effects can be very subtle and hard to predict, due to the complex nature of programs. Therefore, a behavior-based automatic method to analyze the potential effects of different types of bugs is desirable. Such a method could be used to automatically build an FMEA report about the fault effects, or to evaluate different failure mitigation and detection techniques. This paper follows the latter direction, demonstrating the use of a model checking-based automated SW-FMEA approach to evaluate error detection and fault tolerance mechanisms, demonstrated on a case study inspired by safety-critical embedded operating systems.
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Kulcsár, Edina, Tamás Csiszér, and János Abonyi. "Pairwise comparison based failure mode and effects analysis (FMEA)." MethodsX 7 (2020): 101007. http://dx.doi.org/10.1016/j.mex.2020.101007.

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Belu, Nadia, Daniel Constantin Anghel, and Nicoleta Rachieru. "Failure Mode and Effects Analysis on Control Equipment Using Fuzzy Theory." Advanced Materials Research 837 (November 2013): 16–21. http://dx.doi.org/10.4028/www.scientific.net/amr.837.16.

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Failure Mode and Effects Analysis is a methodology to evaluate a system, design, process, machine or service for possible ways in which failures (problems, errors, risks and concerns) can occur and it has been used in a wide range of industries. Traditional method uses a Risk Priority Number to evaluate the risk level of a component or process. This is obtained by finding the multiplication of three factors, which are the severity of the failure (S), the probability/occurrence of the failure (O), and the probability of not detecting the failure (D). There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. Fuzzy logic appears to be a powerful tool for performing a criticality analysis on a system design and prioritizing failure identified in analisys FMEA for corrective actions. In this paper we present a parallel between the typical and the fuzzy computation of RPNs, in order to assess and rank risks associated to failure modes that could appear in the functioning of control equipment.
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Askari, Roohollah, Milad Shafii, Sima Rafiei, Mohammad Sadegh Abolhassani, and Elaheh Salarikhah. "Failure mode and effect analysis: improving intensive care unit risk management processes." International Journal of Health Care Quality Assurance 30, no. 3 (April 18, 2017): 208–15. http://dx.doi.org/10.1108/ijhcqa-04-2016-0053.

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Purpose Failure modes and effects analysis (FMEA) is a practical tool to evaluate risks, discover failures in a proactive manner and propose corrective actions to reduce or eliminate potential risks. The purpose of this paper is to apply FMEA technique to examine the hazards associated with the process of service delivery in intensive care unit (ICU) of a tertiary hospital in Yazd, Iran. Design/methodology/approach This was a before-after study conducted between March 2013 and December 2014. By forming a FMEA team, all potential hazards associated with ICU services – their frequency and severity – were identified. Then risk priority number was calculated for each activity as an indicator representing high priority areas that need special attention and resource allocation. Findings Eight failure modes with highest priority scores including endotracheal tube defect, wrong placement of endotracheal tube, EVD interface, aspiration failure during suctioning, chest tube failure, tissue injury and deep vein thrombosis were selected for improvement. Findings affirmed that improvement strategies were generally satisfying and significantly decreased total failures. Practical implications Application of FMEA in ICUs proved to be effective in proactively decreasing the risk of failures and corrected the control measures up to acceptable levels in all eight areas of function. Originality/value Using a prospective risk assessment approach, such as FMEA, could be beneficial in dealing with potential failures through proposing preventive actions in a proactive manner. The method could be used as a tool for healthcare continuous quality improvement so that the method identifies both systemic and human errors, and offers practical advice to deal effectively with them.
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Shan, Hongmei, Qiaoqiao Tong, Jing Shi, and Qian Zhang. "Risk Assessment of Express Delivery Service Failures in China: An Improved Failure Mode and Effects Analysis Approach." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 6 (September 21, 2021): 2490–514. http://dx.doi.org/10.3390/jtaer16060137.

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With the rapid growth of express delivery industry, service failure has become an increasingly pressing issue. However, there is a lack of research on express service failure risk assessment within the Failure Mode and Effects Analysis (FMEA) framework. To address the research gap, we propose an improved FMEA approach based on integrating the uncertainty reasoning cloud model and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The cloud model describing randomness and fuzziness in uncertainty environment is adopted to achieve the transformation between the qualitative semantic evaluation of occurrence (O), severity (S), and detection (D) risk factors of FMEA and the quantitative instantiation and set up the comprehensive cloud of risk assessment matrix for express delivery service failure (EDSF). The TOPSIS method calculates and ranks the relative closeness coefficients of EDSF mode. Finally, the rationality and applicability of the proposed method are demonstrated by an empirical study for the express delivery service in China. It is found that among 18 express delivery service failure modes, six service failure modes with high risk are mainly located in the processing and delivery stages, while six service failures with the relatively low risk are involved in the picking-up and transportation stages. This study provides insight on how to explore the risk evaluation of express delivery service failure, and it helps express delivery firms to identify the key service failure points, develop the corresponding service remedy measures, reduce the loss from service failures, and improve the service quality.
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Ünlükal, Ceren, Mine Şenel, and Bilgin Şenel. "Risk Assessment with Failure Mode and Effects Analysis and Grey Relational Analysis Method in Plastic Injection Process." ITM Web of Conferences 22 (2018): 01023. http://dx.doi.org/10.1051/itmconf/20182201023.

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This study aims to evaluate the risks that may arise during the production process in a plastic injection manufacturing enterprise with traditional Failure Mode and Effect Analysis (FMEA) and Grey Relational Analysis (GRA). Although it is a widely used analytical technique that helps to identify and reduce the risks of failure in a process, the Failure Mode and Effects Analysis (FMEA) has some drawbacks that the different risks can have the same risk priority values and the weight of risk factors is not take into consideration. This situation has been tried to be eliminated by integrating the FMEA with the GRA. As a result, it is seen that the order of risk priority values of the identified failure changes according to both methods.
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Zhou, Deyun, Yongchuan Tang, and Wen Jiang. "A Modified Model of Failure Mode and Effects Analysis Based on Generalized Evidence Theory." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4512383.

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Due to the incomplete knowledge, how to handle the uncertain risk factors in failure mode and effects analysis (FMEA) is still an open issue. This paper proposes a new generalized evidential FMEA (GEFMEA) model to handle the uncertain risk factor, which may not be included in the conventional FMEA model. In GEFMEA, not only the conventional risk factors, the occurrence, severity, and detectability of the failure mode, but also the other incomplete risk factors are taken into consideration. In addition, the relative importance among all these risk factors is well addressed in the proposed method. GEFMEA is based on the generalized evidence theory, which is efficient in handling incomplete information in the open world. The efficiency and some merit of the proposed method are verified by the numerical example and a real case study on aircraft turbine rotor blades.
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Pires, Ana, and Paula Sobral. "Application of failure mode and effects analysis to reduce microplastic emissions." Waste Management & Research: The Journal for a Sustainable Circular Economy 39, no. 5 (March 29, 2021): 744–53. http://dx.doi.org/10.1177/0734242x211003133.

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A complete understanding of the occurrence of microplastics and the methods to eliminate their sources is an urgent necessity to minimize the pollution caused by microplastics. The use of plastics in any form releases microplastics to the environment. Existing policy instruments are insufficient to address microplastics pollution and regulatory measures have focussed only on the microbeads and single-use plastics. Fees on the use of plastic products may possibly reduce their usage, but effective management of plastic products at their end-of-life is lacking. Therefore, in this study, the microplastic–failure mode and effect analysis (MP–FMEA) methodology, which is a semi-qualitative approach capable of identifying the causes and proposing solutions for the issue of microplastics pollution, has been proposed. The innovative feature of MP–FMEA is that it has a pre-defined failure mode, that is, the release of microplastics to air, water and soil (depending on the process) or the occurrence of microplastics in the final product. Moreover, a theoretical recycling plant case study was used to demonstrate the advantages and disadvantages of this method. The results revealed that MP–FMEA is an easy and heuristic technique to understand the failure-effect-causes and solutions for reduction of microplastics and can be applied by researchers working in different domains apart from those relating to microplastics. Future studies can include the evaluation of the use of MP–FMEA methodology along with quantitative methods for effective reduction in the release of microplastics.
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Bhangu, N. S., Rupinder Singh, and G. L. Pahuja. "Failure Mode and Effect Analysis of a Thermal Power Plant for Enhancing its Reliability." Applied Mechanics and Materials 110-116 (October 2011): 2969–75. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.2969.

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Failure Mode and Effect Analysis (FMEA) has a well deserved reputation for systematic and thorough evaluation of failures at the system, sub-system or component level in all manufacturing and processing sectors. These organizations are looking for the final product to be “safe and reliable”. FMEA helps designers to identify and eliminate/control dangerous failure modes, minimizing damage to the system and its users. This paper, as an extension to the prior research work, introduces an insight into the reasons of failure and its effects in a thermal power plant opted for the case study, based on conceptual designs in context of FMEA. The analysis takes into account preparation of appropriate diagnostic and maintenance procedures with the aim of enhancement of thermal plant reliability. The FMEA technique used may be helpful for the design and maintenance departments to curtail the downtime of the plant.
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Földešiová, Daniela, Maroš Korenko, Martina Ferancová, and Pavol Kaplík. "Analysis of Risk Using FMEA." Advanced Materials Research 801 (September 2013): 81–85. http://dx.doi.org/10.4028/www.scientific.net/amr.801.81.

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Failure mode and effects analysis (FMEA) was performed in RIBE Slovakia, k.s. (limited partnership) in Nitra, Slovakia. Analysis was done in the process of cold forming where potential errors (failures) were identified. Subsequently, there was assigned a mark in the FMEA form expressing severity, frequency and undetectability, and a risk priority number (RPN) was calculated. If the risk priority number (RPN) is too high, corrective measures are proposed and adopted in order for the risk to be minimized or completely eliminated.
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Liu, Baoyu, Yong Hu, and Yong Deng. "New Failure Mode and Effects Analysis based on D Numbers Downscaling Method." International Journal of Computers Communications & Control 13, no. 2 (April 13, 2018): 205–20. http://dx.doi.org/10.15837/ijccc.2018.2.2990.

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Failure mode and effects analysis (FMEA) is extensively applied to process potential faults in systems, designs, and products. Nevertheless, traditional FMEA, classical risk priority number (RPN), acquired by multiplying the ratings of occurrence, detection, and severity, risk assessment, is not effective to process the uncertainty in FMEA. Many methods have been proposed to solve the issue but deficiencies exist, such as huge computing quality and the mutual exclusivity of propositions. In fact, because of the subjectivity of experts, the boundary of two adjacent evaluation ratings is fuzzy so that the propositions are not mutually exclusive. To address the issues, in this paper, a new method to evaluate risk in FMEA based on D numbers and evidential downscaling method, named as D numbers downscaling method, is proposed. In the proposed method, D numbers based on the data are constructed to process uncertain information and aggregate the assessments of risk factors, for they permit propositions to be not exclusive mutually. Evidential downscaling method decreases the number of ratings from 10 to 3, and the frame of discernment from 2^{10} to 2^3 , which greatly reduce the computational complexity. Besides, a numerical example is illustrated to validate the high efficiency and feasibility of the proposed method.
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Nasim, Faria, Joseph T. Poterucha, Lisa M. Daniels, John G. Park, Troy G. Seelhammer, John K. Bohman, Tammy P. Friedrich, Caitlin L. Blau, Jennifer L. Elmer, and Gregory J. Schears. "Practical Implementation of Failure Mode and Effects Analysis for Extracorporeal Membrane Oxygenation Activation." American Journal of Medical Quality 33, no. 5 (January 24, 2018): 523–29. http://dx.doi.org/10.1177/1062860618754703.

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Extracorporeal membrane oxygenation (ECMO) is used to treat severe hypoxemic respiratory failure and as a rescue therapy for patients with cardiopulmonary arrest within a narrow window of time. A failure modes and effects analysis (FMEA) was conducted to analyze the clinical and operational processes leading to delays in initiating ECMO. FMEA determined these highest-risk failure modes that were contributing to process failure: (1) ECMO candidacy not determined in time, (2) no or incomplete evaluation for ECMO prior to consult or arrest, (3) ECMO team not immediately available, and (4) cannulation not completed in time. When implemented collectively, a total of 4 interventions addressed more than 95% of the system failures. These interventions were (1) ECMO response pager held by a team required for decision, (2) distribution of institutionally defined inclusion/exclusion criteria, (3) educational training for clinicians consulting the ECMO team, and (4) establishment of a mobile ECMO insertion cart.
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Liu, Hu-Chen, Jian-Xin You, Xue-Feng Ding, and Qiang Su. "Improving risk evaluation in FMEA with a hybrid multiple criteria decision making method." International Journal of Quality & Reliability Management 32, no. 7 (August 3, 2015): 763–82. http://dx.doi.org/10.1108/ijqrm-10-2013-0169.

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Purpose – The purpose of this paper is to develop a new failure mode and effect analysis (FMEA) framework for evaluation, prioritization and improvement of failure modes. Design/methodology/approach – A hybrid multiple criteria decision-making method combining VIKOR, decision-making trial and evaluation laboratory (DEMATEL) and analytic hierarchy process (AHP) is used to rank the risk of the failure modes identified in FMEA. The modified VIKOR method is employed to determine the effects of failure modes on together. Then the DEMATEL technique is used to construct the influential relation map among the failure modes and causes of failures. Finally, the AHP approach based on the DEMATEL is utilized to obtain the influential weights and give the prioritization levels for the failure modes. Findings – A case study of diesel engine’s turbocharger system is provided to illustrate the potential application and benefits of the proposed FMEA approach. Results show that the new risk priority model can be effective in helping analysts find the high risky failure modes and create suitable maintenance strategies. Practical implications – The proposed FMEA can overcome the shortcomings and improve the effectiveness of the traditional FMEA. Particularly, the dependence and interactions between different failure modes and effects have been addressed by the new failure analysis method. Originality/value – This paper presents a systemic analytical model for FMEA. It is able to capture the complex interrelationships among various failure modes and effects and provide guidance to analysts by setting the suitable maintenance strategies to improve the safety and reliability of complex systems.
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Rehman, Zobia, Claudiu Vasile Kifor, Farhana Jabeen, Sheneela Naz, and Muhammad Waqar. "Automatic Acquisition of Failure Mode and Effect Analysis Ontology for Sustainable Risk Management." Sustainability 12, no. 23 (December 7, 2020): 10208. http://dx.doi.org/10.3390/su122310208.

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In this piece of research, we have presented an approach to populate Failure Mode and Effect Analysis (FMEA) ontology from existing worksheets prepared by experts. FMEA is a commonly used method for risk assessment in any organization. This method is initiated by domain experts who analyze all the associated risks to a product or process, their causes, severity, effects and mitigation actions. Besides domain experts, time and cost are the other two factors involved in successful completion of FMEA. Reusability of the knowledge produced at the end of this method can bring numerous benefits to an organization. Some ontologies are available for semantic content management of FMEA knowledge but in order to avail their full benefits, it is must that they can acquire the existing knowledge automatically. Major objective of this article is to develop an algorithm, which can populate FMEA ontology from existing worksheets. Major contribution of this work is to identify an existing FMEA ontology and its evaluation for schema and relationship richness, then its automatic population using proposed algorithm without human intervention, and finally making it a part of complete knowledge management system. Our proposed algorithm correctly mapped 1357 instances to FMEA ontology from manually prepared FMEA spreadsheets. This FMEA ontology has been queried by domain experts and it was proved to be very helpful in experts like decision-making.
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Zandi, Peyman, Mohammad Rahmani, Mojtaba Khanian, and Amir Mosavi. "Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)." Agriculture 10, no. 11 (October 28, 2020): 504. http://dx.doi.org/10.3390/agriculture10110504.

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Failure mode and effects analysis (FMEA) is a popular technique in reliability analyses. In a typical FMEA, there are three risk factors for each failure modes: Severity (S), occurrence (O), and detectability (D). These will be included in calculating a risk priority number (RPN) multiplying the three aforementioned factors. The literature review reveals some noticeable efforts to overcome the shortcomings of the traditional FMEA. The objective of this paper is to extend the application of FMEA to risk management for agricultural projects. For this aim, the factor of severity in traditional FMEA is broken down into three sub-factors that include severity on cost, the severity on time, and severity on the quality of the project. Moreover, in this study, a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) integrated with a fuzzy analytical hierarchy process (AHP) was used to address the limitations of the traditional FMEA. A sensitivity analysis was done by weighing the risk assessment factors. The results confirm the capability of this Hybrid-FMEA in addressing several drawbacks of the traditional FMEA application. The risk assessment factors changed the risk priority between the different projects by affecting the weights. The risk of water and energy supplies and climate fluctuations and pests were the most critical risk in agricultural projects. Risk control measures should be applied according to the severity of each risk. Some of this research’s contributions can be abstracted as identifying and classifying the risks of investment in agricultural projects and implementing the extended FMEA and multicriteria decision-making methods for analyzing the risks in the agriculture domain for the first time. As a management tool, the proposed model can be used in similar fields for risk management of various investment projects.
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Beyene, Tsehaye Dedimas, Sisay Geremew Gebeyehu, and Azemeraw Tadesse Mengistu. "Application of Failure Mode Effect Analysis (FMEA) to Reduce Downtime in a Textile Share Company." Journal of Engineering, Project, and Production Management 8, no. 1 (January 31, 2018): 40–46. http://dx.doi.org/10.32738/jeppm.201801.0005.

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Zadry, Hilma Raimona, Dendi Adi Saputra, Agung Budiman Tabri, Difana Meilani, and Dina Rahmayanti. "Failure Modes and Effects Analysis (FMEA) for evaluation of a sugarcane machine failure." MATEC Web of Conferences 204 (2018): 01012. http://dx.doi.org/10.1051/matecconf/201820401012.

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The Failure Modes and Effects Analysis (FMEA) method has been widely recognized as a tool that systematically identifies the consequences and failures of the system or process, and reduces or eliminates the chances of the failure. This study applies that method to evaluate the causes of failure in the use of sugarcane machine that have been designed in the previous studies. FMEA approach anticipated the failures at the design stage, so that a more reliable and ergonomic design can be produced for future sugarcane machine. The potential failure identified from the machine consists of capacity issues, machine maintenance, preliminary treatment, and procedures of use. The study found that capacity issues are the priority problems that cause the machine failure. Then, this study proposed some actions to reduce the risk priority number (RPN) on 12 failures.
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Feemster, Agnes Ann, Melissa Augustino, Rosemary Duncan, Anand Khandoobhai, and Meghan Rowcliffe. "Use of failure modes and effects analysis to mitigate potential risks prior to implementation of an intravenous compounding technology." American Journal of Health-System Pharmacy 78, no. 14 (April 23, 2021): 1323–29. http://dx.doi.org/10.1093/ajhp/zxab179.

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Abstract Purpose The purpose of this study was to identify potential failure points in a new chemotherapy preparation technology and to implement changes that prevent or minimize the consequences of those failures before they occur using the failure modes and effects analysis (FMEA) approach. Methods An FMEA was conducted by a team of medication safety pharmacists, oncology pharmacists and technicians, leadership from informatics, investigational drug, and medication safety services, and representatives from the technology vendor. Failure modes were scored using both Risk Priority Number (RPN) and Risk Hazard Index (RHI) scores. Results The chemotherapy preparation workflow was defined in a 41-step process with 16 failure modes. The RPN and RHI scores were identical for each failure mode because all failure modes were considered detectable. Five failure modes, all attributable to user error, were deemed to pose the highest risk. Mitigation strategies and system changes were identified for 2 failure modes, with subsequent system modifications resulting in reduced risk. Conclusion The FMEA was a useful tool for risk mitigation and workflow optimization prior to implementation of an intravenous compounding technology. The process of conducting this study served as a collaborative and proactive approach to reducing the potential for medication errors upon adoption of new technology into the chemotherapy preparation process.
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Shaker, Fatemeh, Arash Shahin, and Saeed Jahanyan. "Developing a two-phase QFD for improving FMEA: an integrative approach." International Journal of Quality & Reliability Management 36, no. 8 (September 2, 2019): 1454–74. http://dx.doi.org/10.1108/ijqrm-07-2018-0195.

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Purpose The purpose of this paper is to propose an integrative approach for improving failure modes and effects analysis (FMEA). Design/methodology/approach An extensive literature review on FMEA has been performed. Then, an integrative approach has been proposed based on literature review. The proposed approach is an integration of FMEA and quality function deployment (QFD). The proposed approach includes a two-phase QFD. In the first phase, failure modes are prioritized based on failure effects and in the second phase, failure causes are prioritized based on failure modes. The proposed approach has been examined in a case example at the blast furnace operation of a steel-manufacturing company. Findings Results of the case example indicated that stove shell crack in hot blast blower, pump failure in cooling water supply pump and bleeder valves failed to operate are the first three important failure modes. In addition, fire and explosion are the most important failure effects. Also, improper maintenance, over pressure and excess temperature are the most important failure causes. Findings also indicated that the proposed approach with the consideration of interrelationships among failure effects, failure mode and failure causes can influence and adjust risk priority number (RPN) in FMEA. Research limitations/implications As manufacturing departments are mostly dealing with failure effects and modes of machinery and maintenance departments are mostly dealing with causes of failures, the proposed model can support better coordination and integration between the two departments. Such support seems to be more important in firms with continuous production lines wherein line interruption influences response to customers more seriously. A wide range of future study opportunities indicates the attractiveness and contribution of the subject to the knowledge of FMEA. Originality/value Although the literature indicates that in most of studies the outcomes of QFD were entered into FMEA and in some studies the RPN of FMEA was entered into QFD as importance rating, the proposed approach is a true type of the so-called “integration of FMEA and QFD” because the three main elements of FMEA formed the structure of QFD. In other words, the proposed approach can be considered as an innovation in the FMEA structure, not as a data provider prior to it or a data receiver after it.
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Bonato, Jasminka, Martina Badurina, and Julijan Dobrinić. "Parameters Assessment of the FMEA Method by Means of Fuzzy Logic." Journal of Maritime & Transportation Science 2, Special edition 2 (April 2018): 123–32. http://dx.doi.org/10.18048/2018-00.123.

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The paper aims at presenting the FMEA method based on the fuzzy technique, representing a new approach to the failure analysis and its effects on the observed system. The FMEA (Failure Mode and Effect Analysis) method has assigned the risks a coefficient i.e. a numerical indicator that very clearly defines the degree of risk. The risk is calculated as a mathematical function of RPN which depends on the effects S, probability O that some case will lead to a failure and to a probability that a failure D can not be detected before its effects are realized. RPN = S O D. The FMEA method, based on the fuzzy logic, makes a more reliable evaluation of the observed system failures possible.
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Jeon, Jennifer, Sylvia Hyland, Catherine M. Burns, and Kathryn Momtahan. "Challenges with applying FMEA to the process for reading labels on injectable drug containers." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 11 (October 2007): 735–39. http://dx.doi.org/10.1177/154193120705101128.

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As a part of a study that aims to evaluate and improve the labelling of containers for injectable drugs, Failure Mode and Effects Analysis (FMEA) was applied to the label reading process. Implementing a FMEA on a small-scale cognitive process involved various challenges including difficulties in representing the process, defining the failure modes, causes and effects, developing the rating scales for criticality, and rating the criticality of the failure modes. The failure modes were rated via two focus groups of healthcare professionals. The results highlight complexities and potential pitfalls with applying FMEA to the label reading process.
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Liou, James J. H., Perry C. Y. Liu, and Huai-Wei Lo. "A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis." Mathematics 8, no. 12 (December 1, 2020): 2145. http://dx.doi.org/10.3390/math8122145.

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Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users’ safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes.
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Cupşan, Vlad Ciprian, Mihail Aurel Țîţu, and Gheorghe Ioan Pop. "Enhancements to Failure Mode and Effects Analysis (FMEA) Method, Aiming to Improve Risk Management in the Knowledge-Based Organizations." International conference KNOWLEDGE-BASED ORGANIZATION 25, no. 1 (June 1, 2019): 206–12. http://dx.doi.org/10.2478/kbo-2019-0033.

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Abstract This paper presents the current state of risk management in the knowledge-based organisations and the importance of a preventive approach, with emphasis on the aerospace and defence industry, as well as gives detailed information on the Failure Mode and Effects Analysis (FMEA) method, in its current known state. In order to generate enhancements to the Failure Mode and Effects Analysis (FMEA) method, the strong and weak points are analysed and specific solutions are proposed for the weak points, such as occurrence scoring, with the goal of enhancing the Failure Mode and Effects Analysis (FMEA) method and, in general, of improving risk management in the knowledge-based organizations. In conclusion, the paper evaluates the improvements generated by the proposed solutions, compared to the current known method, in order to establish the value added by the enhancements.
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Xu, Zhengzheng, Soyoung Lee, David Albani, Donald Dobbins, Rodney J. Ellis, Tithi Biswas, Mitchell Machtay, and Tarun K. Podder. "Evaluating radiotherapy treatment delay using Failure Mode and Effects Analysis (FMEA)." Radiotherapy and Oncology 137 (August 2019): 102–9. http://dx.doi.org/10.1016/j.radonc.2019.04.016.

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Chirmule, Narendra, Ravindra Khare, Atul Khandekar, and Vibha Jawa. "Failure Mode and Effects Analysis (FMEA) for Immunogenicity of Therapeutic Proteins." Journal of Pharmaceutical Sciences 109, no. 10 (October 2020): 3214–22. http://dx.doi.org/10.1016/j.xphs.2020.07.019.

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Kmenta, Steven, and Koshuke Ishii. "Scenario-Based Failure Modes and Effects Analysis Using Expected Cost." Journal of Mechanical Design 126, no. 6 (November 1, 2004): 1027–35. http://dx.doi.org/10.1115/1.1799614.

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Failure modes and effects analysis (FMEA) uses the product of three ranked factors to compute the risk priority number (RPN). Unfortunately, the RPN components have ambiguous definitions, and multiplying ranked values is not a valid operation. As a result, the RPN produces inconsistent risk priorities. In addition, FMEA uses distinct analyses for each system level and life cycle phase, making it difficult to consolidate interrelated risk information. The goal of scenario-based FMEA is to delineate and evaluate risk events more accurately. Probability and cost provide a consistent basis for risk analysis and decision making, and failure scenarios provide continuity across system levels and life cycle phases.
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Bognár, Ferenc, and Petra Benedek. "Case Study on a Potential Application of Failure Mode and Effects Analysis in Assessing Compliance Risks." Risks 9, no. 9 (September 9, 2021): 164. http://dx.doi.org/10.3390/risks9090164.

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Assessing and reducing compliance risks can now be considered one of the core criteria for business success. While failure mode and effect analysis (FMEA) is widely used in engineering, its application in the financial sector is quite novel, primarily related to compliance risk assessment. This paper presents the results of exploratory research based on the potential application of FMEA in a focus group of compliance experts at one of the largest Central and Eastern European commercial banks. This study aims to establish a process for assessing compliance risks that builds on the strengths of both the qualitative and quantitative assessment methods. Applying FMEA based on a nominal group technique and further statistical analysis provides an opportunity to compare expert assessments and the consensus level of the participants. As a result, the similarity or difference of the assessment patterns can be quantified, providing objective feedback on the evaluation. Finally, this paper proposes lifting the detectability of failures as an evaluation dimension to the same level of importance as the probability and impact of non-compliance and using agreement testing statistical methods.
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Sharma, Kapil Dev, and Shobhit Srivastava. "Failure Mode and Effect Analysis (FMEA) for Enhancing Reliability of Water Tube Boiler in Thermal Power Plant." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 8, no. 02 (December 25, 2016): 79–86. http://dx.doi.org/10.18090/samriddhi.v8i2.7139.

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Failure mode and effect analysis is one of the QS-9000 quality system requirement supplements, with a wide applicability in all industrial fields. FMEA is the inductive failure analysis instruments which can be defined as a methodical group of activities intended to recognize and evaluate the potential failure modes of a product/ process and its effects with an aim to identify actions which could eliminate or reduce the chance of the potential failure before the problem occur. The purpose of this paper is to evaluate the FMEA research and application in the Thermal Power Plant Industry. The research will highlight the application of FMEA method to water tubes (WT) in boilers with an aim to find-out all the major and primary causes of boiler failure and reduce the breakdown for continuous power generation in the plant. Failure Mode and Effect Analysis technique is applied on most critical or serious parts (components) of the plant which having highest Risk Priority Number (RPN). Comparison is made between the quantitative results of FMEA and reliability field data from real tube systems. These results are discussed to establish relationships which are useful for future water tube designs.
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Jain, Khushboo. "Use of failure mode effect analysis (FMEA) to improve medication management process." International Journal of Health Care Quality Assurance 30, no. 2 (March 13, 2017): 175–86. http://dx.doi.org/10.1108/ijhcqa-09-2015-0113.

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Purpose Medication management is a complex process, at high risk of error with life threatening consequences. The focus should be on devising strategies to avoid errors and make the process self-reliable by ensuring prevention of errors and/or error detection at subsequent stages. The purpose of this paper is to use failure mode effect analysis (FMEA), a systematic proactive tool, to identify the likelihood and the causes for the process to fail at various steps and prioritise them to devise risk reduction strategies to improve patient safety. Design/methodology/approach The study was designed as an observational analytical study of medication management process in the inpatient area of a multi-speciality hospital in Gurgaon, Haryana, India. A team was made to study the complex process of medication management in the hospital. FMEA tool was used. Corrective actions were developed based on the prioritised failure modes which were implemented and monitored. Findings The percentage distribution of medication errors as per the observation made by the team was found to be maximum of transcription errors (37 per cent) followed by administration errors (29 per cent) indicating the need to identify the causes and effects of their occurrence. In all, 11 failure modes were identified out of which major five were prioritised based on the risk priority number (RPN). The process was repeated after corrective actions were taken which resulted in about 40 per cent (average) and around 60 per cent reduction in the RPN of prioritised failure modes. Research limitations/implications FMEA is a time consuming process and requires a multidisciplinary team which has good understanding of the process being analysed. FMEA only helps in identifying the possibilities of a process to fail, it does not eliminate them, additional efforts are required to develop action plans and implement them. Frank discussion and agreement among the team members is required not only for successfully conducing FMEA but also for implementing the corrective actions. Practical implications FMEA is an effective proactive risk-assessment tool and is a continuous process which can be continued in phases. The corrective actions taken resulted in reduction in RPN, subjected to further evaluation and usage by others depending on the facility type. Originality/value The application of the tool helped the hospital in identifying failures in medication management process, thereby prioritising and correcting them leading to improvement.
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Ozilgen, Sibel. "Failure Mode and Effect Analysis (FMEA) for confectionery manufacturing in developing countries: Turkish delight production as a case study." Food Science and Technology 32, no. 3 (August 7, 2012): 505–14. http://dx.doi.org/10.1590/s0101-20612012005000083.

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The Failure Mode and Effect Analysis (FMEA) was applied for risk assessment of confectionary manufacturing, in whichthe traditional methods and equipment were intensively used in the production. Potential failure modes and effects as well as their possible causes were identified in the process flow. Processing stages that involve intensive handling of food by workers had the highest risk priority numbers (RPN = 216 and 189), followed by chemical contamination risks in different stages of the process. The application of corrective actions substantially reduced the RPN (risk priority number) values. Therefore, the implementation of FMEA (The Failure Mode and Effect Analysis) model in confectionary manufacturing improved the safety and quality of the final products.
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Chang, Tai-Wu, Huai-Wei Lo, Kai-Ying Chen, and James Liou. "A Novel FMEA Model Based on Rough BWM and Rough TOPSIS-AL for Risk Assessment." Mathematics 7, no. 10 (September 20, 2019): 874. http://dx.doi.org/10.3390/math7100874.

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Failure mode and effects analysis (FMEA) is a risk assessment method that effectively diagnoses a product’s potential failure modes. It is based on expert experience and investigation to determine the potential failure modes of the system or product to develop improvement strategies to reduce the risk of failures. However, the traditional FMEA has many shortcomings that were proposed by many studies. This study proposes a hybrid FMEA and multi-attribute decision-making (MADM) model to establish an evaluation framework, combining the rough best worst method (R-BWM) and rough technique for order preference by similarity to an ideal solution technique (R-TOPSIS) to determine the improvement order of failure modes. In addition, this study adds the concept of aspiration level to R-TOPSIS technology (called R-TOPSIS-AL), which not only optimizes the reliability of the TOPSIS calculation program, but also obtains more potential information. This study then demonstrates the effectiveness and robustness of the proposed model through a multinational audio equipment manufacturing company. The results show that the proposed model can overcome many shortcomings of traditional FMEA, and effectively assist decision-makers and research and development (R&D) departments in improving the reliability of products.
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Sagnak, Muhittin, Yigit Kazancoglu, Yesim Deniz Ozkan Ozen, and Jose Arturo Garza-Reyes. "Decision-making for risk evaluation: integration of prospect theory with failure modes and effects analysis (FMEA)." International Journal of Quality & Reliability Management 37, no. 6/7 (April 20, 2020): 939–56. http://dx.doi.org/10.1108/ijqrm-01-2020-0013.

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PurposeThe aim of the present study is to overcome some of the limitations of the FMEA method by presenting a theoretical base for considering risk evaluation into its assessment methodology and proposing an approach for its implementation.Design/methodology/approachFuzzy AHP is used to calculate the weights of the likelihood of occurrence (O), severity (S) and difficulty of detection (D). Additionally, the prospect-theory-based TODIM method was integrated with fuzzy logic. Thus, fuzzy TODIM was employed to calculate the ranking of potential failure modes according to their risk priority numbers (RPNs). In order to verify the results of the study, in-depth interviews were conducted with the participation of industry experts.FindingsThe results are very much in line with prospect theory. Therefore, practitioners may apply the proposed method to FMEA. The most crucial failure mode for a firm's attention is furnace failure followed by generator failure, crane failure, tank failure, kettle failure, dryer failure and operator failure, respectively.Originality/valueThe originality of this paper consists in integrating prospect theory with the FMEA method in order to overcome the limitations naturally inherent in the calculation of the FMEA's RPNs.
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Yu, Shih-Heng, Emily Su, and Yi-Tui Chen. "Data-Driven Approach to Improving the Risk Assessment Process of Medical Failures." International Journal of Environmental Research and Public Health 15, no. 10 (September 20, 2018): 2069. http://dx.doi.org/10.3390/ijerph15102069.

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In recent decades, many researchers have focused on the issue of medical failures in the healthcare industry. A variety of techniques have been employed to assess the risk of medical failure and to generate strategies to reduce the frequency of medical failures. Considering the limitations of the traditional method—failure mode and effects analysis (FMEA)—for risk assessment and quality improvement, this paper presents two models developed using data envelopment analysis (DEA). One is called the slacks-based measure DEA (SBM-DEA) model, and the other is a novel data-driven approach (NDA) that combines FMEA and DEA. The relative advantages of the three models are compared. In this paper, an infant security case consisting of 16 failure modes at Western Wake Medical Center in Raleigh, North Carolina, U.S., was employed. The results indicate that both SBM-DEA and NDA may improve the discrimination and accuracy of detection compared to the traditional method of FMEA. However, NDA was found to have a relative advantage over SBM-DEA due to its risk assessment capability and precise detection of medical failures.
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Liu, Baoyu, and Yong Deng. "Risk Evaluation in Failure Mode and Effects Analysis Based on D Numbers Theory." INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL 14, no. 5 (November 17, 2019): 672. http://dx.doi.org/10.15837/ijccc.2019.5.3558.

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Failure mode and effects analysis (FMEA) is a useful technology for identifying the potential faults or errors in system, and simultaneously preventing them from occurring. In FMEA, risk evaluation is a vital procedure. Many methods are proposed to address this issue but they have some deficiencies, such as the complex calculation and two adjacent evaluation ratings being considered to be mutually exclusive. Aiming at these problems, in this paper, A novel method to risk evaluation based on D numbers theory is proposed. In the proposed method, for one thing, the assessments of each failure mode are aggregated through D numbers theory. For another, the combination usage of risk priority number (RPN) and the risk coefficient newly defined not only achieve less computation complexity compared with other methods, but also overcome the shortcomings of classical RPN. Furthermore, a numerical example is illustrated to demonstrate the effectiveness and superiority of the proposed method.
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Kricke, Gayle Shier, Matthew B. Carson, Young Ji Lee, Corrine Benacka, R. Kannan Mutharasan, Faraz S. Ahmad, Preeti Kansal, Clyde W. Yancy, Allen S. Anderson, and Nicholas D. Soulakis. "Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification." Journal of the American Medical Informatics Association 24, no. 2 (September 1, 2016): 288–94. http://dx.doi.org/10.1093/jamia/ocw083.

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Objective: Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning. Methods: Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation. Results: EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation. Conclusion: Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.
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Rachieru, Nicoleta, Nadia Belu, and Daniel Constantin Anghel. "Improvement of Process Failure Mode and Effects Analysis Using Fuzzy Logic." Applied Mechanics and Materials 371 (August 2013): 822–26. http://dx.doi.org/10.4028/www.scientific.net/amm.371.822.

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Risk analysis increased in importance within environmental, health and safety regulation last few years. Process Failure Mode and Effects Analysis (PFMEA) is one of the most used techniques to evaluate a process for strengths, weaknesses, potential problem areas or failure modes, and to prevent problems before they occur. The traditional PFMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. Fuzzy logic approach is preferable in order to remove the deficiencies in assigning the risk priority numbers. In this study, a fuzzy-based FMEA is to be applied to improve the manufacturing process of rear bumper, injection part used in automotive industry. The fuzzy model PFMEA can provide the stability of process assurance.
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de Aguiar, Dimas Campos, Valério Antonio Pamplona Salomon, and Carlos Henrique Pereira Mello. "An ISO 9001 based approach for the implementation of process FMEA in the Brazilian automotive industry." International Journal of Quality & Reliability Management 32, no. 6 (June 1, 2015): 589–602. http://dx.doi.org/10.1108/ijqrm-09-2013-0150.

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Purpose – The purpose of this paper is to present a structured way for the definitions of the Process Failure Mode and Effect Analysis (FMEA) attributes, such as potential failure mode, potential cause and potential effect, in order to make it simpler to define the controls and scores. Design/methodology/approach – This study performs, through a case study in incoming inspection of raw material, the comparison of a conventional application of the Process FMEA with a proposal based on the concepts of process approach defined by ISO 9001. Findings – Even written in a form similar to a script, the application of Process FMEA is a very complex activity and, like most quality tools, before being applied, FMEA should be clearly understood by the team. One way to facilitate this understanding is considering the sequence of events in the failures analysis to understand their causes and effects, just as are the sequences of inputs and outputs in the definition of the process approach addressed in ISO 9001. Originality/value – This paper shows a simple way to better structure Process FMEA, facilitating meetings with multidisciplinary teams.
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Cardoso, Priscila, Marotta Flávia, and Siqueira Kelen. "RISK ANALYSIS EVALUATION OF PIROXICAM 20MG CAPSULES MANUFACTURE PROCESS BY FMEA TECHNIQUE." International Journal of Drug Regulatory Affairs 6, no. 1 (March 15, 2018): 19–25. http://dx.doi.org/10.22270/ijdra.v6i1.215.

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This paper presents an application of FMEA (Failure Mode and Effects Analysis) on the manufacturing process of piroxicam 20mg capsules, with the objective of identifying possible failures and create subsidies for improvement of the production stages. Brainstorming or Collaborative critical analysis was used as a tool to collect information and support FMEA’s application. After the risk analysis, evaluation of the production process using FMEA, manipulation and secondary packaging were found to be the critical stages in the production process necessary for implementation of actions in order to mitigate the risks.
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