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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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Kulkarni, Chetan S., Matteo Corbetta, and Elinirina Robinson. "Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 12. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1297.
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This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles.The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks,
thus introducing dependability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded whitin FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric powertrain system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.
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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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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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Cavaignac, Andre Luis De Oliveira, and Jhelison Gabriel Lima Uchoa. "Obtaining FMEA’s indices for occupational safety in civil construction: a theoretical contribution." Brazilian Journal of Operations & Production Management 15, no. 4 (November 25, 2018): 558–65. http://dx.doi.org/10.14488/bjopm.2018.v15.n4.a9.
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Highlights: 1- Severity, occurrence and detection indices were obtained for specific use in construction safety; 2- Important theoretical contribution to the use of FMEA in safety of construction work; 3- Obtaining the S, O and D indices better suited to civil construction will encourage the use of FMEA in the area; 4- A quick reference table of S, O and D indexes was proposed for use by occupational safety professionals; 5- The quick reference table will allow the RPNs of the fault modes to be safely compared between different situations and different applicators.
Goal: The achievement of FMEA indices better adapted to the area of occupational safety in construction. From a quick reference table, the use of the FMEA will be facilitated by professionals in the area.
Design / Methodology / Approach: For the elaboration of this work were carried out researches in the literature available in scientific journals on the subject. To obtain the severity tables, the conversion of concepts of maintenance and reliability to concepts of accident severity was performed. For the occurrence table the Ford Handbook model was used (FORD, 2011), and as the database of accident statistics the most up-to-date social security yearbook was used (FAZENDA, 2016). For the detection table, a detection index model was proposed that was discussed based on commonly used risk management procedures and tools.
Results: Individual tables were obtained for each FMEA index. The indices were adapted to the reality of the application of FMEA in work safety in construction. From the individual tables, a quick reference table containing the three FMEA indices related to the qualitative scale of each was obtained.
Limitations of the investigation: The study limits itself to adapting the FMEA indices for work safety in construction. This study may serve as a basis for future studies on obtaining the FMEA indexes for work safety applied in other areas of activities, requiring adequate scientific sources. Regarding the validation of the indices, it is noticeable the difficulty of comparing these indices proposed in this work with indices applied subjectively and without scientific reference, relying only on the skill and previous experience of the applicator. However, it is reasonable to say that the FMEAS applied with the indices obtained in this work will have a better accuracy in representing the reality, regardless of the applicator's ability.
Practical implications: Reduce the difficulties in choosing the S, O and D indices for the application of FMEA in construction safety, reduce the inaccuracy in obtaining the risk priority number for failure modes and diffuse the use of FMEA for risk analysis and prevention occupations in construction are the main theoretical implications of this work.
Originality / Value: there are studies in the literature on the application of FMEA in various areas - maintenance and product development, for example - but there is very little research on the application of FMEA in occupational safety. In addition, FMEA application studies cite the difficulty of choosing the S, O and D indices, but there is an absence of studies seeking solutions to this imprecision. In this sense, this work seeks to contribute to a choice of the FMEA indexes, which is easier and more efficient due to the better adaptation of the same to the occupational safety area in civil construction.
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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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Di Nardo, Mario, Teresa Murino, Gianluca Osteria, and Liberatina Carmela Santillo. "A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in An Agri-Food Company." Applied System Innovation 5, no. 3 (April 24, 2022): 45. http://dx.doi.org/10.3390/asi5030045.
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The Failure Mode and Effect Analysis (FMEA) is often used to improve a system’s reliability. This paper proposes a new approach that aims to overcome the most critical defects of the traditional FMEA. This new methodology combines the Entropy and Best Worst Method (BWM) methodology with the EDAS and System Dynamics, FMECA: The EN-B-ED Dynamic FMECA. The main innovation point of the proposed work is the presence of an unknown factor (Cost) that allows to obtain an objective weighted factor, a risk index when a machine failure occurs. The criticality analysis has been carried out using software (Vensim PLE x64) to simulate System Dynamics models to identify corrective actions and evaluate the possible implementation of these actions. The methodology proposed is applied to a case study in a relevant Italian company in the agri-food sector.
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Di Nardo, Mario, Teresa Murino, Gianluca Osteria, and Liberatina Carmela Santillo. "A New Hybrid Dynamic FMECA with Decision-Making Methodology: A Case Study in An Agri-Food Company." Applied System Innovation 5, no. 3 (April 24, 2022): 45. http://dx.doi.org/10.3390/asi5030045.
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The Failure Mode and Effect Analysis (FMEA) is often used to improve a system’s reliability. This paper proposes a new approach that aims to overcome the most critical defects of the traditional FMEA. This new methodology combines the Entropy and Best Worst Method (BWM) methodology with the EDAS and System Dynamics, FMECA: The EN-B-ED Dynamic FMECA. The main innovation point of the proposed work is the presence of an unknown factor (Cost) that allows to obtain an objective weighted factor, a risk index when a machine failure occurs. The criticality analysis has been carried out using software (Vensim PLE x64) to simulate System Dynamics models to identify corrective actions and evaluate the possible implementation of these actions. The methodology proposed is applied to a case study in a relevant Italian company in the agri-food sector.
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Santos, Ricardo, Laura Caldeira, and João Serra. "Aplicação da FMEA/FMECA a uma barragem de retenção de rejeitados." Geotecnia, no. 114 (November 21, 2008): 113–42. http://dx.doi.org/10.14195/2184-8394_114_6.
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Neste artigo apresenta-se um enquadramento geral das análises de riscos em Geotecnia, perspec - ti vando-se os seus objectivos e os respectivos domínios de aplicação. Referem-se, em pormenor, os seguintes mé todos de análise de risco: análise dos modos de rotura e seus efeitos (FMEA) e análise dos modos de rotura, seus efeitos e sua criticalidade (FMECA). Aplicam-se a FMEA e a FMECA a uma barragem de aterro con ven - cio nal para retenção de rejeitados. A utilização de análises de riscos em Geotecnia permite detectar e controlar atem padamente os eventuais problemas e gerir os riscos, reduzindo-os, tão cedo e tão eficientemente quanto pos sível. Reconhecidas as vantagens das análises de riscos, admite-se que estas possam vir a desempenhar uma função central das actividades desenvolvidas em programas de segurança de obras geotécnicas im por tan tes.
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Lee, Dongwoo, Dongmin Lee, and Jongwhoa Na. "Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model." Applied Sciences 12, no. 12 (June 16, 2022): 6144. http://dx.doi.org/10.3390/app12126144.
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In the development of safety-critical systems, it is important to perform failure modes and effects analysis (FMEA) to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an automatic FMEA tool that integrates the model-based design (MBD), FMEA, and simulated fault injection techniques in a single environment. The automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool: First, the automatic FMEA tool automatically generates FMEA reports, unlike the traditional spreadsheet-based FMEA tools. Second, the automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the automatic FMEA were compared to those of the legacy FMEA.
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Мирошников, Вячеслав, Vyacheslav Miroshnikov, Олег Горленко, Oleg Gorlenko, Николай Борбаць, and Nikolay Borbats. "QUALITY MANAGEMENT IN DESIGN-TECHNOLOGICAL PREPARATION OF ENGINEERING PRODUCE MANUFACTURING BASED ON COMPLEX FMEA-ANALYSIS." Bulletin of Bryansk state technical university 2016, no. 1 (March 31, 2016): 178–87. http://dx.doi.org/10.12737/18313.
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Under presentday conditions the main constituent in the competitiveness of any enterprise – a capaci-ty for ensuring ever-growing requirements of a con-sumer market and concrete customers to produce quality. One of the ways to solve this not simple problem is the development and introduction of an ever-improving system of quality management (SQM) meeting the highest international requirements. A significant requirement of international standards is the application in SQM a procedure of the FMEA-analysis of kinds and consequences of failures which is used for more than half a century abroad at the creation of complex engineering systems.
To date a considerable urgency gains the introduction of the complex FMEA-methodology including the fulfillment of a product design analysis, the PFMEA-analysis of the product manufacturing method and the MFMEA-analysis of equipment (rigging). The peculiarity of such a complex analysis consists in the hierarchy and consistency of the ful-fillment various kinds of FMEA: FMEA of product – FMEA of an assembly (A) – FMEA of a part. All kinds of FMEA are connected and depend on each other. The FMEA result of product and assembly design is a basis for the FMEA of product and unit assembly. It is evident that the last should be carried out after the introduction of changes in the design of a unit according to the FMEA results of design. The FMEA of equipment should be carried out after the introduction of changes in an engineering procedure according to the FMEA results of a process. Taking into account this, first should be carried out the FMEA of a design and the FMEA of a process and the FMEA of equipment should complete the consistency of tests. The paper reports thoroughly the first two stages of the complex FMEA-analysis.
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Zhou, Xuelian, and Yongchuan Tang. "Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades." Entropy 20, no. 11 (November 9, 2018): 864. http://dx.doi.org/10.3390/e20110864.
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As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this new perspective, a modified FMEA approach is proposed, where the subjective uncertainty of FMEA experts is handled in the framework of Dempster–Shafer evidence theory (DST). In the improved FMEA approach, the ambiguity measure (AM) which is an entropy-like uncertainty measure in DST framework is applied to quantify the uncertainty degree of each FMEA expert. Then, the classical risk priority number (RPN) model is improved by aggregating an AM-based weight factor into the RPN function. A case study based on the new RPN model in aircraft turbine rotor blades verifies the applicable and useful of the proposed FMEA approach.
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Mulyasari, Dewi, I. Wayan Suweca, and Rachman Setiawan. "Penerapan Metode Failure Mode, Effect And Criticality Analiysis (Fmeca) Pada Rem Mekanik Sub Komponen Alat Angkut Konveyor Rel." Sistemik : Jurnal Ilmiah Nasional Bidang Ilmu Teknik 7, no. 1 (July 9, 2019): 1–4. http://dx.doi.org/10.53580/sistemik.v7i1.4.
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Abstrak
Pengertian dari FMECA (Failure Mode Effect and Criticality Analysis) adalah suatu metode untuk mengevaluasi ataupun mendesain dari komponen pada suatu sistem dengan cara meneliti potensi modus kegagalannya untuk menentukan dampak yang akan terjadi pada komponen atau sistem kerja. Setiap potensi dari suatu modus kegagalan diklasifikasikan berdasarkan dampak yang dapat ditimbulkan pada keberhasilan sistem tersebut ataupun pada keselamatan pengguna dan peralatan, sehingga dapat diketahui kemungkinan kondisi paling kritis pada komponen yang akan terjadi.
Alat transportasi batubara yang terbaru dengan rangkaian gerbong yang digerakkan oleh sistem penggerak luar yaitu konveyor rel. Dalam aplikasinya yang akan digunakan untuk mengangkut batubara di area tambang dari sebuah perusahaan tambang nasional dengan jarak antara loading station dan unloading station sekitar 2,3 km. Konveyor rel terdiri dari 8 sub sistem, salah satunya adalah drive station yang merupakan komponen penggerak dari Konveyor rel yang dipasang pada lokasi-lokasi tertentu. Drive Station sendiri terdiri dari 5 komponen utama, yaitu panel daya listrik, sistem kontrol, unit penggerak, mekanisme pendorong dan rem mekanik. Rem mekanik sebagai komponen utama diharapkan memiliki sub komponen yang memadai. Untuk itu, jurnal ini akan membahas nilai kekritisan pada setiap sub komponen dari rem mekanik dengan menggunakan pendekatan FMECA.
Pengumpulan data sebagai awal dari analisis dimulai dengan rancangan dasar dan cara kerja komponen-komponen utama. Tahap selanjutnya adalah penerapan metode FMECA, yang menggabungkan prosedur FMEA (Failure Mode and Effect Analysis) dan CA (Criticality Analysis). Prosedur FMEA terdiri dari penentuan sistem yang akan dianalisis, membuat diagram blok fungsi sistem, mengidentifikasi modus-modus kegagalan dan mengidentifikasi effect yang ditimbulkan oleh modus kegagalan tersebut. Sedangkan prosedur CA terdiri dari dua prosedur, yaitu perhitungan nilai kekritisan dan melakukan pemeringkatan berdasarkan modus kegagalan. Hasil analisis dari 6 sub komponen dari komponen rem mekanik, didapatkan 1 sub komponen yang memiliki nilai kekritisan tertinggi, yaitu pompa rem mekanik dengan nilai kekritisan sebesar 0,633.
Kata kunci :
Konveyor rel, Drive Station, FMECA, FMEA, CA.
Abstract
Understanding of FMECA (Failure Mode Effect and Critical Analysis) is a method for evaluating or designing components on a system by examining the potential of Failure Mode to determine the impact that will occur on components or work systems. Every potential of a failure mode is classified based on the impact that can be caused on the success of the system or on the safety of users and equipment, so that the most critical possible conditions in the component will be known.
The latest coal transportation with railway coach and driven by an external drive system, rail conveyors. In the application that will be used to transport coal in the mine area from a national mining company with a distance between loading station and unloading station around 2.3 km. Rail conveyors consist of 8 sub-systems, one of the sub-system is a drive station which is a driving component of rail conveyors installed in certain locations. The Drive Station itself consists of 5 main components, namely the electric power panel, control system, drive unit, driving mechanism and mechanical brake. Mechanical brakes as the main component are expected to have adequate sub-components. For this reason, this journal will discuss the critical value of each sub-component of a mechanical brake using the FMECA approach. Data collection as the beginning of the analysis begins with the basic design and workings of the main components. The next stage is the application of the FMECA method, which combines the procedures of FMEA (Failure Mode and Effect Analysis) and CA (Critical Analysis). The FMEA procedure consists of determining the system to be analyzed, making block diagrams of system functions, identifying failure modes and identifying the effects caused by the failure mode. While the CA procedure consists of two procedures, namely the calculation of critical values ??and ranking according to failure mode. The results of the analysis of 6 sub-components of the mechanical brake component, obtained 1 sub-component that has the highest critical value, namely the pump with a critical value of 0.633.
Keywords : Konveyor rel, Drive Station, FMECA, FMEA, CA.
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Widianti, Tri, and Himma Firdaus. "PENGUJIAN SUHU LEMARI ES DENGAN METODE TERINTEGRASI FUZZYFAILURE MODE AND EFFECT ANALYSIS (FUZZY-FMEA)." Jurnal Standardisasi 18, no. 1 (May 9, 2018): 9. http://dx.doi.org/10.31153/js.v18i1.693.
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Failure Mode and Effect Analysis (FMEA) banyak diimplementasikan untuk analisis risiko baik di bidang manufaktur maupun jasa. Permasalahan yang sering timbul pada implementasi FMEA yaitu sulitnya menentukan peringkat risiko karena kesamaan nilai RPN. Samanya nilai RPN menimbulkan kesulitan bagi pengambil keputusan untuk memprioritisasi risiko yang harus ditindaklanjuti. Logika fuzzy merupakan logika matematis yang dapat digunakan untuk memperbaiki kelemahan FMEA. Sehingga, tujuan penelitian ini adalah integrasi FMEA dengan logika fuzzy sebagai upaya perbaikan terhadap metode FMEA. Tujuan lainnya adalah implementasi integrasi Fuzzy-FMEA pada lingkup pengujian suhu lemari es. Implementasi Fuzzy-FMEA pada pengujian ini dilakukan sebagai tindakan pencegahan terhadap risiko kegagalan pada pengujian yang dipersyaratkan oleh SNI ISO/IEC 17025:2008. Studi kasus pengujian suhu pada lemari es ini dipilih karena lemari es merupakan salah satu produk yang diwajibkan untuk memperoleh Sertifikat Produk Penggunaan Tanda SNI (SPPT-SNI) yang mengacu pada standar SNI IEC 60335-2-7:2009. Selain itu, penerapan Fuzzy-FMEA pada konteks pengujian sampai saat ini belum ditemukan. Hasil analisis dengan Fuzzy-FMEA menunjukkan bahwa risiko kegagalan paling tinggi pada proses pengujian suhu lemari es paling tinggi terjadi pada mode kegagalan: power source tibatiba shut down dengan nilai RPN 5,8887.
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Yoo, Jae Min, Dong Geun Ahn, and Joong Soon Jang. "Review of FMEA." Journal of Applied Reliability 19, no. 4 (December 31, 2019): 318–33. http://dx.doi.org/10.33162/jar.2019.12.19.4.318.
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Pistone, Cheryl. "FMEA & SCHMEMEA." Gastroenterology Nursing 29, no. 2 (March 2006): 167. http://dx.doi.org/10.1097/00001610-200603000-00073.
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Nugroho, Saputra Dwi. "Evaluasi Distributed Control System pada PLTU dengan Failure Mode, Effect and Criticaly Analysis (FMECA)." KILAT 11, no. 1 (April 10, 2022): 88–102. http://dx.doi.org/10.33322/kilat.v11i1.1531.
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Steam power plants (PLTU) are power plants that have the largest percentage of power plants owned by PLN. The main controller in a PLTU usually uses a Distributed Control System (DCS). The reliability of DCS in a PLTU must be maintained properly so that the power plant does not experience a control failure that causes the PLTU to stop suddenly. DCS PLTU Sebalang Unit 1 has used the FMEA method to determine its maintenance strategy, but the FMEA has not defined the function of the equipment, functional failure, the effect of failure, and criticality analysis (CA) of the equipment. failure mode prioritization has not been carried out. With the FMECA conducted in this study, the priority of the failure mode can be carried out effectively so that the priority determination of DCS maintenance can be carried out. Results of the FMECA that have been carried out, it is known that the failure that occurs in the DCS CPU has the highest RPN value (140). The maintenance strategy obtained from the FMECA results is preventive maintenance (PM), namely: 1) checking the power supply voltage, 2) checking the communication status, idle time status, load and CPU status, and maintenance run to failure (RTF) CPU replacement if there is damage to the CPU. FCS CPUs.
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Setiasih, Putri Ingen, and Purnawan Junadi. "Effectiveness Of Failure Modes Effect Analysis (FMEA)." Journal of Indonesian Health Policy and Administration 2, no. 2 (July 17, 2017): 25. http://dx.doi.org/10.7454/ihpa.v2i2.1971.
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Objective : One of the prevention efforts of medical errors that occur in health services is by identifying the potential failure of the service before the failure occurs. FMEA is one way to identify the risks of failure. Therefore, the authors wanted to find out whether FMEA was effective for reducing Medical error based on previous studies.Method: This article was a literature review using references in an online database such as EBSCOhost. The author found 280 articles while searching by using the keyword "FMEA". After filtered by publication period from 2012 to 2017, fulltext and language, finally got 7 articles. Finally, the author used the three most relevant literature.Result: FMEA is proven to decrease potential failure rate after follow-up to failure was done in service process, so medical error can be prevented. In the application of FMEA, bias can occur during the determination of potential failure and determination of scoring on RPN. Therefore, it is expected that the team involved in making FMEA experts in the process of service to be designed.Conclusion: FMEA could prevent medical errors by determining potential failure and following up on potential failure before failure occurs.Keywords: FMEA, healthcare, medical error.
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Krisdiono, Eko, I. Nengah Putra A, I. Made Jiwa Astika, and Bambang Suharjo. "AN ANALYSIS ON KRI IMPROVEMENT PROJECT IN INDONESIAN NAVAL MAIN BASE NO. V SURABAYA FASHARKAN (NAVY’S BATTLESHIPS MAINTENANCE AND REPAIRING FACILITIES USING FUZZY FMEA METHOD." JOURNAL ASRO 10, no. 1 (May 7, 2019): 77. http://dx.doi.org/10.37875/asro.v10i1.93.
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The FMEA method is a tool used to identify the consequences or consequences of a system or process failure and reduce or eliminate failure. In determining risk factors and system improvement priorities, traditional FMEA still has weaknesses, where FMEA traditionally places severity factors, occurrence, and detection at the same level of importance, even though in reality different levels of importance and the importance of FMEA assessment teams are ignored. In this study, the fuzzy method is integrated into FMEA where the severity factor, occurrence, and detection are assessed in linguistic form. In this fuzzy method, the weight of the interests of the FMEA assessment team is taken into account to do ranking and repair priorities. The application of Fuzzy methods to FMEA to determine significant risk factors and prioritizing improvements from various alternatives chosen for the process of repairing KRI in Surabaya Lantamal V, so that it is expected that the application of this method can improve the operational performance of Fasharkan to eliminate or reduce the risks that occur in the repair process ships in the eastern region of the fleet.
Keywords: Risk Management, Failure Modes and Effect Analysis (FMEA), Fuzzy Method
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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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Geramian, Arash, Mohammad Reza Mehregan, Nima Garousi Mokhtarzadeh, and Mohammadreza Hemmati. "Fuzzy inference system application for failure analyzing in automobile industry." International Journal of Quality & Reliability Management 34, no. 9 (October 2, 2017): 1493–507. http://dx.doi.org/10.1108/ijqrm-03-2016-0026.
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Purpose Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry. Design/methodology/approach Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran. Findings Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization. Practical implications This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system. Originality/value This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.
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Visych, S. Yu, S. V. Rusanova, L. M. Andryukova, and A. B. Dorovskyy. "Analysis and risk assessment method fmea/fmeca for the biowaiver procedure." Upravlìnnâ, ekonomìka ta zabezpečennâ âkostì v farmacìï, no. 4(52) (November 30, 2017): 4–11. http://dx.doi.org/10.24959/uekj.17.38.
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Spreafico, Christian, Davide Russo, and Caterina Rizzi. "A state-of-the-art review of FMEA/FMECA including patents." Computer Science Review 25 (August 2017): 19–28. http://dx.doi.org/10.1016/j.cosrev.2017.05.002.
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Min, Seungsik, and Hyeonae Jang. "Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data." Applied Sciences 11, no. 16 (August 10, 2021): 7349. http://dx.doi.org/10.3390/app11167349.
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Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.
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Tang, Yongchuan, Yonghao Zhou, Ying Zhou, Yubo Huang, and Deyun Zhou. "Failure Mode and Effects Analysis on the Air System of an Aero Turbofan Engine Using the Gaussian Model and Evidence Theory." Entropy 25, no. 5 (May 6, 2023): 757. http://dx.doi.org/10.3390/e25050757.
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Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.
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Utami, Anisa Sri, Rahmi Fahmy, and Zifriyanthi Minanda Putri. "Peran Metode Failure Mode and Effect Analysis (FMEA) terhadap Mutu Pelayanan Rumah Sakit: Systematik Review." Jurnal Ilmiah Universitas Batanghari Jambi 20, no. 3 (October 1, 2020): 932. http://dx.doi.org/10.33087/jiubj.v20i3.1080.
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Incidence of medication errors, nosocomial infections, falling patients, pressure sores (pressure sores) is often found in hospitals. This incident can be prevented by identifying the risk and finding the root of the problem using Failure mode and effect analysis (FMEA). This study aims to describe the effectiveness of the FMEA method on the quality of hospital services using a systematic review method. This research starts from searching for data using five databases with the keywords "FMEA or Risk Management or HFMEA" and "service quality" and "hospital" then doing screenings and feasibility assessments of 17 articles. The results of this study were seventeen articles describing the FMEA method in several hospital service processes where the evaluation results of the FMEA method could improve service quality. Therefore the FMEA method is effectively applied to the service process starting from identifying the risk of failure in service, implementing preventive measures to improve the quality of hospital services in the form of security dimensions, effectiveness, and efficiency. So this study recommends the use of the FMEA method in identifying the risk of failure to improve service quality.
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ERBAY, Barbaros, and Coşkun ÖZKAN. "Fuzzy FMEA Application Combined with Fuzzy Cognitive Maps to Manage the Risks of a Software Project." European Journal of Engineering and Formal Sciences 2, no. 2 (June 12, 2018): 7. http://dx.doi.org/10.26417/ejef.v2i2.p7-22.
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The failure rate of an Information Technologies (IT) software project is pretty high because of their uncertain and risky structure. Managing well this kind of projects becomes important. Failure Mode and Effect Analysis (FMEA) is an extensive method that is used for identifying the importance level of risks in a project by using risk priority numbers (RPN). This method is based on experts’ experience and cognitive skills at gathering data in order to make risk assessment. This situation causes inaccurate conclusions in the final risk ranking. Fuzzy logic is widely integrated into FMEA to handle these inaccuracies and inconsistencies in the literature while making assessment and calling Fuzzy FMEA method that we proposed. In this study, we explored another uncovered weaknesses of the proposed method. FMEA and Fuzzy FMEA do not consider the relationships among the risks of a project. To overcome this disadvantage, we proposed to integrate the idea of cognitive maps into these two methods (FMEA w/FCMs and Fuzzy FMEA w/FCMs). Finally, we got a comprehensive risk assessment methodology by considering the relationships among the risks under ambiguous circumstances.
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ERBAY, Barbaros, and Coşkun ÖZKAN. "Fuzzy FMEA Application Combined with Fuzzy Cognitive Maps to Manage the Risks of a Software Project." European Journal of Engineering and Formal Sciences 3, no. 1 (April 12, 2018): 7. http://dx.doi.org/10.26417/ejef.v3i1.p7-22.
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The failure rate of an Information Technologies (IT) software project is pretty high because of their uncertain and risky structure. Managing well this kind of projects becomes important. Failure Mode and Effect Analysis (FMEA) is an extensive method that is used for identifying the importance level of risks in a project by using risk priority numbers (RPN). This method is based on experts’ experience and cognitive skills at gathering data in order to make risk assessment. This situation causes inaccurate conclusions in the final risk ranking. Fuzzy logic is widely integrated into FMEA to handle these inaccuracies and inconsistencies in the literature while making assessment and calling Fuzzy FMEA method that we proposed. In this study, we explored another uncovered weaknesses of the proposed method. FMEA and Fuzzy FMEA do not consider the relationships among the risks of a project. To overcome this disadvantage, we proposed to integrate the idea of cognitive maps into these two methods (FMEA w/FCMs and Fuzzy FMEA w/FCMs). Finally, we got a comprehensive risk assessment methodology by considering the relationships among the risks under ambiguous circumstances.
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Zuniawan, Akhyar. "A Systematic Literature Review of Failure Mode and Effect Analysis (FMEA) Implementation in Industries." IJIEM - Indonesian Journal of Industrial Engineering and Management 1, no. 2 (November 30, 2020): 59. http://dx.doi.org/10.22441/ijiem.v1i2.9862.
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Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs, or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized for having many shortcomings. Various risk priority models have been proposed in various literature to improve the performance of the FMEA itself. However, there has been no literature review on this topic. This study reviewed 50 FMEA papers published between 1998 and 2019 in international journals and categorized them according to various industry and industry output. The automotive and manufacturing industries dominated the implementation of FMEA. For the industry's production: goods and services, mostly dominated by interests in implementing FMEA in their industries. Hopefully, this finding will be useful for goods and services industries willing to implement FMEA, especially the services industry.
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Wolniak, Radosław. "Problems of use of FMEA method in industrial enterprise." Production Engineering Archives 23, no. 23 (June 1, 2019): 12–17. http://dx.doi.org/10.30657/pea.2019.23.02.
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Abstract Presented paper concentrate on problems connected with FMEA method usage in industrial enterprise. There is in the paper a description of the basic rules of FMEA method and competition between FMEA analysis and gap analysis. The analysis of defects has been done to find recommendations how to eliminate or restrain them. On the basis of conducted research we found that selection of staff to the team is very important factor in the FMEA analysis undertaking process. The staff should have appropriate level of knowledge about FMEA method methodology and other tools which are indispensable in the process of implementing this method within the company.
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Sutrisno, Agung, Indra Gunawan, Iwan Vanany, Mohammad Asjad, and Wahyu Caesarendra. "An improved modified FMEA model for prioritization of lean waste risk." International Journal of Lean Six Sigma 11, no. 2 (October 26, 2018): 233–53. http://dx.doi.org/10.1108/ijlss-11-2017-0125.
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Purpose Proposing an improved model for evaluating criticality of non-value added (waste) in operation is necessary for realizing sustainable manufacturing practices. The purpose of this paper is concerning on improvement of the decision support model for evaluating risk criticality lean waste occurrence by considering the weight of modified FMEA indices and the influence of waste-worsening factors causing the escalation of waste risk magnitude. Design/methodology/approach Integration of entropy and Taguchi loss function into decision support model of modified FMEA is presented to rectify the limitation of previous risk reprioritization models in modified FMEA studies. The weight of the probability components and loss components are quantified using entropy. A case study from industry is used to test the applicability of the integration model in practical situation. Findings The proposed model enables to overcome the limitations of using subjective determination on the weight of modified FMEA indices. The inclusion of the waste-worsening factors and Taguchi loss functions enables the FMEA team to articulate the severity level of waste consequences appropriately over the use of ordinal scale in ranking the risk of lean waste in modified FMEA references. Research limitations/implications When appraising the risk of lean waste criticality, ignorance on weighting of FMEA indices may be inappropriate for an accurate risk-based decision-making. This paper provides insights to scholars and practitioners and others concerned with the lean operation to understand the significance of considering the impact of FMEA indices and waste-worsening factors in evaluating criticality of lean waste risks. Practical implications The method adopted is for quantifying the criticality of lean waste and inclusion of weighting of FMEA indices in modified FMEA provides insight and exemplar on tackling the risk of lean waste and determining the most critical waste affecting performability of company operations. Originality/value Integration of the entropy and Taguchi loss function for appraising the criticality of lean waste in modified FMEA is the first in the lean management discipline. These findings will be highly useful for professionals wishing to implement the lean waste reduction strategy.
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García Aguirre, Pedro Angel, Luis Pérez-Domínguez, David Luviano-Cruz, Jesús Jaime Solano Noriega, Erwin Martínez Gómez, and Mauro Callejas-Cuervo. "PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design." Applied Sciences 11, no. 4 (February 4, 2021): 1406. http://dx.doi.org/10.3390/app11041406.
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Product Design (PD) currently faces challenges in new product development, since the industry is in a rush to introduce new products into the market, with customers demanding products that are faster, cheaper, and free from failure. In addition, global companies are trying to improve their product design risk assessment process to gain advantages over competitors, using proven tools like Failure Mode and Effect Analysis (FMEA) and mixing risk assessment methods. However, with current risks assessment tools and a combination of other methods, there is the opportunity to improve risk analysis. This document aims to reveal a novel integrated method, where FMEA, Pythagorean Fuzzy Sets (PFS), and Dimensional Analysis (DA) are cohesive in one model. The proposed method provides an effective technique to identify risks and remove uncertainty and vagueness of human intervention during risk assessment using the Failure Mode and Effect Analysis method. A real-life problem was carried out to illustrate the proposed method. Finally, the study was substantiated by using a correlation and sensitivity analysis, demonstrating the presented integrated method’s usefulness in decision-making and problem-solving.
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Erbay, Barbaros, and Coşkun Özkan. "Fuzzy FMEA Application Combined with Fuzzy Cognitive Maps to Manage the Risks of a Software Project." European Journal of Engineering and Formal Sciences 2, no. 2 (August 1, 2018): 6–21. http://dx.doi.org/10.2478/ejef-2018-0007.
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Abstract The failure rate of an Information Technologies (IT) software project is pretty high because of their uncertain and risky structure. Managing well this kind of projects becomes important. Failure Mode and Effect Analysis (FMEA) is an extensive method that is used for identifying the importance level of risks in a project by using risk priority numbers (RPN). This method is based on experts’ experience and cognitive skills at gathering data in order to make risk assessment. This situation causes inaccurate conclusions in the final risk ranking. Fuzzy logic is widely integrated into FMEA to handle these inaccuracies and inconsistencies in the literature while making assessment and calling Fuzzy FMEA method that we proposed. In this study, we explored another uncovered weaknesses of the proposed method. FMEA and Fuzzy FMEA do not consider the relationships among the risks of a project. To overcome this disadvantage, we proposed to integrate the idea of cognitive maps into these two methods (FMEA w/FCMs and Fuzzy FMEA w/FCMs). Finally, we got a comprehensive risk assessment methodology by considering the relationships among the risks under ambiguous circumstances.
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Koçak, Sinan, Edit Tóth-Laufer, and László Pokorádi. "Advanced fuzzy rule-based failure mode and effect analysis." Gradus 8, no. 3 (2021): 187–94. http://dx.doi.org/10.47833/2021.3.eng.001.
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This article aims to show few advanced approach method for improving Failure Mode and Effect Analysis (FMEA). The possible failure situations in the system and design are investigated separately and considered in a Hierarchical FMEA worksheet form. The authors propose three different methodological methods based on hybridized fuzzy logic and FMEA.
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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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Idris, Mohd Firdaus Mohamad, and Nor Hayati Saad. "Mid-Life Refurbishment Maintenance Strategy to Sustain Performance and Reliability of Train System." Applied Mechanics and Materials 899 (June 2020): 238–52. http://dx.doi.org/10.4028/www.scientific.net/amm.899.238.
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Mid-Life Refurbishment (MLR) is a process conducted in many industries to improve or extend the life span of assets such as machines, infrastructures and systems. The objective of MLR works execution is to extend the life span, sustain the train performance and achieve system reliability. Typically, the refurbishment scope consists of overhaul, upgrading and rectification works. The biggest challenge is to determine) the scope of refurbishment works and to create equilibrium between the feasibility and viability of the project. Therefore, the main objective of this paper is to discuss the current practice of performing MLR maintenance through the utilization of Failure Mode and Effect Analysis (FMEA) and to transform the existing FMEA used by the automotive industry to fulfil the needs of Rolling Stock. The use of FMEA is critical in determining) the scope of train refurbishment work. It was carried out at the initial stages of the process in order to determine Risk Priority Number (RPN) to prioritize the type of refurbishment plans and scope. The design of the FMEA Worksheets, De-sign of FMEA severity Evaluation Criteria, Design of Occurrence Evaluation Criteria and Design of FMEA Prevention/Detection Criteria were adopted and adapted from the generic format so that it is coherent with Railway Industries. The results based on the transformation framework, the plan and scope of overhaul, upgrading and rectification were defined using the FMEA. Out of the 80 elements of MLR works that were analyzed using the FMEA approach, it was found that 46 elements needed overhaul, 23 elements needed upgrading and 11 elements needed rectification works. Finally, the application of the FMEA helped determine the MLR scope of work from 13 systems. The case study was taken from the Malaysia LRT Project, and currently the fleet has been in operation for more than 20 years (operated since year 1998).
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S Suharyo, Okol, Avando Bastari, and Abdul Rahman. "DETERMINATION OF CRITICAL COMPONENTS FOR NAVIGATION RADAR USING FUZZY FMEA AND TOPSIS." JOURNAL ASRO 9, no. 2 (December 3, 2018): 115. http://dx.doi.org/10.37875/asro.v9i2.82.
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In determining the critical components and repair priorities, traditional FMEA still have weaknesses, which puts the traditional FMEA factor severity, occurance and detection at the same level of importance, despite the fact that have different levels of interest and importance weight FMEA assessment teams are ignored. In this study integrated fuzzy method in which the FMEA factor severity, occurance and detection assessed in the form of linguistics. At this fuzzy method, the weight of the assessment team FMEA interests are taken into account. To do perangkingan and priority repair used method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on criteria such as level of risk, economic costs, availability of spare parts, maintenance of economic safety and personal abilities. Application of Fuzzy and TOPSIS method in the FMEA to determine the critical components and priorities of the various alternative repair elected to damage components applied to Sperry Marine Navigation Radar system, which is expected by the application of this method can improve operational performance KRI to keep the areas of national jurisdiction
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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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Godina, Radu, Beatriz Gomes Rolis Silva, and Pedro Espadinha-Cruz. "A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry." Applied Sciences 11, no. 8 (April 20, 2021): 3726. http://dx.doi.org/10.3390/app11083726.
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The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.
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Aguiar, Dimas Campos de, and Valério A. P. Salomon. "Avaliação da prevenção de falhas em processos utilizando métodos de tomada de decisão." Production 17, no. 3 (December 2007): 502–19. http://dx.doi.org/10.1590/s0103-65132007000300008.
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Este trabalho apresenta uma investigação sobre o emprego de FMEA (Failure Mode and Effect Analysis) de Processo com a exposição de irregularidades na sua utilização. O método AHP (Analytic Hierarchy Process) e os Conjuntos Fuzzy são aplicados no estudo das práticas atuais de utilização de FMEA. O AHP é aplicado para a priorização das irregularidades quanto à gravidade de sua ocorrência. Os Conjuntos Fuzzy são aplicados para avaliação do desempenho da utilização de FMEA em algumas empresas do ramo automotivo. Como resultado, tem-se a aceitação de oito e a não aceitação de três dos onze formulários de FMEA averiguados.
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Vodenicharova, Maria. "Opportunities for the applications of FMEA Model in logistics processes in Bulgarian enterprises." Logistics & Sustainable Transport 8, no. 1 (May 24, 2017): 31–41. http://dx.doi.org/10.1515/jlst-2017-0003.
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Abstract The article explores the use of FMEA method in the logistics processes in manufacturing plants in Bulgaria. The surveyed enterprises have a system ISO 9001 and apply different methods of analysis and assessment of logistics processes. The purpose of this study is to present a model for improving the reliability of logistics processes through the FMEA (Failure Mode and Effect Analysis) method. An inquiry among 14 organizations in the implementation of FMEA was conducted. The results show that FMEA is not used for assessment in logistics processes and provides useful insights for decision-making to improve the reliability of supply. A framework based on the survey is presented for determining the reliability of logistics processes in manufacturing plants. The study demonstrates the applicability of the method in logistics processes and the role FMEA can play in assessing logistics processes.
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Hunt, J. E., C. J. Price, and M. H. Lee. "Automating the FMEA process." Intelligent Systems Engineering 2, no. 2 (1993): 119. http://dx.doi.org/10.1049/ise.1993.0012.
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Webber, Janet. "FMEA: Quality Assurance Methodology." Industrial Management & Data Systems 90, no. 7 (July 1990): 21–23. http://dx.doi.org/10.1108/02635579010002046.
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Ginn, D. M., D. V. Jones, H. Rahnejat, and M. Zairi. "The “QFD/FMEA interface”." European Journal of Innovation Management 1, no. 1 (April 1998): 7–20. http://dx.doi.org/10.1108/14601069810198448.
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Cassanelli, G., G. Mura, F. Fantini, M. Vanzi, and B. Plano. "Failure Analysis-assisted FMEA." Microelectronics Reliability 46, no. 9-11 (September 2006): 1795–99. http://dx.doi.org/10.1016/j.microrel.2006.07.072.
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Price, C. J., and N. S. Taylor. "Automated multiple failure FMEA." Reliability Engineering & System Safety 76, no. 1 (April 2002): 1–10. http://dx.doi.org/10.1016/s0951-8320(01)00136-3.
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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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Ianni, Andrea, Roberto Virgili, Michela Piredda, Maria Grazia De Marinis, and Tommasangelo Petitti. "Healthcare Safety Management: The Model of Fmea/Fmeca in Anatomic Pathology Service." Madridge Journal of Internal and Emergency Medicine 2, no. 1 (April 20, 2018): 50–53. http://dx.doi.org/10.18689/mjiem-1000110.
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Sini, Jacopo, Andrea Passarino, Stefano Bonicelli, and Massimo Violante. "A Simulation-Based Approach to Aid Development of Software-Based Hardware Failure Detection and Mitigation Algorithms of a Mobile Robot System." Sensors 22, no. 13 (June 21, 2022): 4665. http://dx.doi.org/10.3390/s22134665.
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Mechatronic systems, like mobile robots, are fairly complex. They are composed of electromechanical actuation components and sensing elements supervised by microcontrollers running complex embedded software. This paper proposes a novel approach to aid mobile robotics developers in adopting a rigorous development process to design and verify the robot’s detection and mitigation capabilities against random hardware failures affecting its sensors or actuators. Unfortunately, assessing the interactions between the various safety/mission-critical subsystem is quite complex. The failure mode effect analysis (FMEA) alongside an analysis of the failure detection capabilities (FMEDA) are the state-of-the-art methodologies for performing such an analysis. Various guidelines are available, and the authors decided to follow the one released by AIAG&VDA in June 2019. Since the robot’s behavior is based on embedded software, the FMEA has been integrated with the hardware/software interaction analysis described in the ECSS-Q-ST-30-02C manual. The core of this proposal is to show how a simulation-based approach, where the mechanical and electrical/electronic components are simulated alongside the embedded software, can effectively support FMEA. As a benchmark application, we considered the mobility system of a proof-of-concept assistance rover for Mars exploration designed by the D.I.A.N.A. student team at Politecnico di Torino. Thanks to the adopted approach, we described how to develop the detection and mitigation strategies and how to determine their effectiveness, with a particular focus on those affecting the sensors.
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Hasbullah, Hasbullah, Muhammad Kholil, and Dwi Aji Santoso. "ANALISIS KEGAGALAN PROSES INSULASI PADA PRODUKSI AUTOMOTIVE WIRES (AW) DENGAN METODE FAILURE MODE AND EFFECT ANALYSIS (FMEA) PADA PT JLC." SINERGI 21, no. 3 (November 15, 2017): 193. http://dx.doi.org/10.22441/sinergi.2017.3.006.
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FMEA (Failure Mode Effect Analysis) adalah metode yang digunakan dalam mengidentifikasi kemungkinan kegagalan pada proses, fungsi dan design produk sehingga diketahui penyebab dan akibatnya untuk meningkatkan mutu dan reliabilitas produk. Kegagalan proses insulasi pada proses produksi AW (Automotive Wire) mengganggu kinerja produksi PT JLC. FMEA digunakan untuk mengidentifikasi dan mencegah potensi kegagalan proses insulasi pada produksi produk AW (Automotive Wire) di PT JLC . FMEA diulas oleh banyak riset sebagai metode efektif dan dijadikan format standar yang digunakan oleh industri otomotif dalam membuat daftar potensi kegagalan sehingga dapat mengetahui penyebab, dampak dan tindakan pencegahan dalam mengatasinya. FMEA menyediakan metode dalam membuat daftar potensi kegagalan produk AW (Automotive Ware) melalui penilaian kuantitatif dengan kriteria tiga aspek yaitu Tingkat kemungkinan frekwensi terjadi kegagalan (O=Occurence), Tingkat resiko akibat kegagalan (S=Severity) dan Tingkat kemungkinan bisa dideteksi (D=Detection). Dari hasil perhitungan dan analisis FMEA maka dihasilkan daftar urutan prioritas potensi kegagalan proses insulasi melalui perhitungan pada tiga aspek Occurence (O), Severity (S) dan Detection (D) disertai kemungkinan penyebab, dampak dan solusinya. Dua potensi kegagalan terbesar adalah Ketidaksesuaian warna (terlau tua atau muda), marking tidak tercetak jelas dan permukaan insulasi yang kasar. FMEA mampu mengidentifikasi penyebab, dampak dan pencegahan untuk mengantisipasi kegagalan tersebut.