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1
OLUWOLE, AYODELE OLUWASEGUN, BABATUNDE OMONIYI ODEDAIRO, and VICTOR OLUWASINA OLADOKUN. "MANAGING SUPPLY CHAIN RISKS: A FUZZY-FAILURE MODE AND EVALUATION APPROACH FOR RANKING THREATS." Journal of Engineering Studies and Research 27, no. 4 (December 15, 2021): 60–69. http://dx.doi.org/10.29081/jesr.v27i4.300.
Повний текст джерелаАнотація:
On the backdrop of lower transportation cost, outsourcing paved the way for borderless production activities and ushered in the era of Supply Chain Management (SCM). For many organizations, achieving the goals of their Supply Chain (SC) is constantly threatened by increased competition and disruption. In this study, the aim is to identify, and rank, SC threats in a developing country using Failure Mode and Effects Analysis (FMEA) with Fuzzy Logic (FL). FMEA parameters were derived for 44 supply chain threats (SCT1 – SCT44) and their Risk Priority Number (RPN) determined. Subsequently, the Mamdani Fuzzy Inference system was utilized to arrive at a Fuzzy-RPN with 125 rules using severity as a determining factor. The rules were ranked to prioritize SC threats. From the conventional FMEA, demand variation (SCT42) and long-distance sourcing (SCT27) had the highest and lowest RPN, respectively. After fuzzification and defuzzification, Fuzzy-RPN identified raw material delay (SCT1), government policy (SCT11), poor transport infrastructure (SCT18) and political instability (SCT19) as threats with the highest Fuzzy-RPN (210) and product recalls (SCT28) with the lowest Fuzzy-RPN (99). Based on these results, it is concluded that a Fuzzy-FMEA approach can identify and rank SC threats with the use of an RPN devoid of sentiments and inaccuracies.
2
Wu, Xiaojun, and Jing Wu. "The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty." Complexity 2021 (February 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/8817667.
Повний текст джерелаАнотація:
The risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. This model can effectively realize real-time, dynamic, and long-term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations.
3
Ofanson U, Tamunodukobipi D.T, and Nitonye S. "Failure mode effects and criticality analysis (FMECA) using fuzzy logic for ship dynamic positioning (DP) systems." Global Journal of Engineering and Technology Advances 13, no. 1 (October 30, 2022): 038–52. http://dx.doi.org/10.30574/gjeta.2022.13.1.0170.
Повний текст джерелаАнотація:
Predicting the failure modes effect and criticality analysis (FMECA) of a dynamic positioning (DP) system using fuzzy logic is the aim of this research. The identification of DP systems and subsystems, the classification of failure modes into critical and less-critical levels based on the Risk Priority Number (RPN) to depict the main root causes of failure in the DP system are some critical objectives in support of this goal. The analysis offers details on a number of issues, including the causes of failure modes and their effects on the functionality and dependability of equipment. Based on the information provided, it was determined that a number of failure modes produced identical RPN values, and that the ranking scale was erroneous. A new method was tested but could not really prioritize the failure modes with same RPN because of the few choices in the severity, occurrence and detection template. To compensate for this, excel ranking function was employed putting severity, occurrence and detection as key criteria for ranking. Due to the high severity and occurrence index, the RPN ranking results show that the faulty DP system component identified for the scenario SSTs (F1) is categorized as very critical. SSCs (F12), SSPr (F7), SSTs(F2), and SSPs(F15) are additional critical failures. In the study, data analysis and validation were done using a fuzzy rule system based on MATLAB. From the findings, it can be inferred that the failure modes F1, F2, F5, F6, F7, F8, F10, F11, and F15 have values of a similar type of RPN. According to the initial RPN risk level results; there are 19 failure modes in the medium risk level, 2 in the low risk level and 1 in the high risk level. In the final RPN-based risk level results, there are 18 failure scenarios in the low risk level and 4 in the medium risk level. In contrast, there are 5 failure modes in the fuzzy RPN low risk level and 17 failure scenarios in the medium risk level. Without fuzzy logic, the justification score on traditional FMECA can be given directly. This makes traditional FMECA ABS show greater risk than fuzzy FMECA. The failure modes with the highest RPN values were treated as critical parts, so it was recommended that the highest value of RPN be given special attention by making the necessary repairs or replacements in order to lengthen the equipment's lifespan.
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Jee, Tze Ling, Kai Meng Tay, and Chee Khoon Ng. "Enhancing a Fuzzy Failure Mode and Effect Analysis Methodology with an Analogical Reasoning Technique." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (November 20, 2011): 1203–10. http://dx.doi.org/10.20965/jaciii.2011.p1203.
Повний текст джерелаАнотація:
In this paper, a fuzzy Failure Mode and Effect Analysis (FMEA) methodology incorporating an analogical reasoning technique is presented. FMEA methodology was introduced as a formal and systematic procedure for evaluation of risk associated with potential failure modes in the 1960s. Bowles and Peláez [1] proposed a Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model as an alternative to the conventional RPN model. For an FIS-based RPN (a three-input FIS model), a large set of fuzzy rules are required, and it is tedious to collect the full set of rules. With the grid partition strategy, the number of fuzzy rules required increases in an exponential manner, and this phenomenon is known as the “curse of dimensionality” or the combinatorial rule explosion problem. Hence, a rule selection and similarity reasoning technique, i.e., Approximate Analogical Reasoning Schema (AARS) technique are implemented in a fuzzy FMEA in order to solve the problem. The experiment was conducted using a set of data collected from a semiconductor manufacturing line, i.e., underfill dispensing process, and promising results were obtained.
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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.
Повний текст джерелаАнотація:
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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Rachieru, Nicoleta, Nadia Belu, and Daniel Constantin Anghel. "Evaluating the Risk of Failure on Injection Pump Using Fuzzy FMEA Method." Applied Mechanics and Materials 657 (October 2014): 976–80. http://dx.doi.org/10.4028/www.scientific.net/amm.657.976.
Повний текст джерелаАнотація:
This research is aimed at utilizing failure mode and effect analysis (FMEA) which is a reliability analysis method applicable to rotary injection pump design. In traditional FMEA, Risk Priority Number (RPN) ranking system is used to evaluate, the risk level of failures to rank failures and to prioritize actions. RPN is obtained by multiplying the scores of three risk factors like the Severity (S), Occurrence (O) and Detection (D) of each failure mode. RPN method can not emphasise the nature of the problem, which is multi-attributable and has a group of experts' opinions. Furthermore, attributes are subjective and have different importance levels. In this paper, a framework is proposed to overcome the shortcomings of the traditional method through the fuzzy set theory. Two case studies have been shown to demonstrate the methodology thus developed. It is illustrated a parallel between the results obtained by the traditional method and fuzzy logic for determining the RPNs. We expect that fuzzy FMEA model will assist FMEA team in assess and rank risks more precisely compared with risk assessment model of method.
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Domán, László. "Fuzzy FMEA risk assessment approach for IFF system in military helicopters using Matlab R2022A." Katonai Logisztika 30, no. 1-2 (2022): 101–29. http://dx.doi.org/10.30583/2022-1-2-101.
Повний текст джерелаАнотація:
In this article, the author reviews the fuzzy Failure Mode and Effect Analysis (fuzzy FMEA) as one of the effective methods of risk assess- ment. Using the fuzzy logic toolbox of the MATLAB R2022a software, the Identification Friend or Foe (IFF) system of military helicopters is analysed in a case study, during which the fuzzy risk priority numbers (F-RPN) for this system of military helicopters is created. Finally, the author of this paper summarizes the results obtained.
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Karamoozian, Amirhossein, and Desheng Wu. "A hybrid risk prioritization approach in construction projects using failure mode and effective analysis." Engineering, Construction and Architectural Management 27, no. 9 (May 9, 2020): 2661–86. http://dx.doi.org/10.1108/ecam-10-2019-0535.
Повний текст джерелаАнотація:
PurposeConstruction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.Design/methodology/approachThe proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.FindingsThe results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.Originality/valueThe originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.
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Belu, Nadia, Daniel Constantin Anghel, and Nicoleta Rachieru. "Application of Fuzzy Logic in Design Failure Mode and Effects Analysis." Applied Mechanics and Materials 371 (August 2013): 832–36. http://dx.doi.org/10.4028/www.scientific.net/amm.371.832.
Повний текст джерелаАнотація:
Failure Mode and Effects Analysis (FMEA) is one of the basic and the most used techniques of quality management that is used for continuous improvements in product or process designs. While applying this technique, determining the Risk Priority Numbers (RPN), which indicate the levels of risks associated with potential problems, is of prime importance for the success of application. A traditional RPN is obtained as product of three risk factors: occurrence, severity and detection. Values of these factors are generally attained from past experience and this way of risk assessment sometimes leads to inconsistencies and inaccuracies during priority numbering. Fuzzy logic approach is considered a promising solution in order to give a more accurate ranking of potential risks. This paper presented a fuzzy model, in order to assess and rank risks associated to failure modes that could appear in the functioning of a headlining product used in automotive industry.
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Made Ayu Chyntia Dewi Puspitaloka and Yurida Ekawati. "Analisis Perbaikan Kualitas Proses Produksi di PT. XYZ Dengan Menggunakan Metode Fuzzy FMEA." Jurnal Teknik Industri UMC 2, no. 1 (August 16, 2022): 14–26. http://dx.doi.org/10.33479/jtiumc.v2i1.19.
Повний текст джерелаАнотація:
Pengendalian kualitas merupakan tindakan perbaikan dan evaluasi untuk mencegah dan meminimalkan produk cacat dari hasil produksi. PT. XYZ adalah perusahaan yang bergerak dibidang industri makanan roti yang proses pembuatannya menggunakan tenaga manusia dan mesin. PT. XYZ memproduksi beberapa roti yaitu COK2, CKPJ, KW, dan SMSJ. Selama proses produksi di PT. XYZ ditemukan produk afkir yang harus dilakukan sebuah perbaikan. Metode analisis yang digunakan adalah FMEA dengan pendekatan fuzzy. Metode FMEA dapat mengidentifikasi potensi kegagalan, namun memiliki kelemahan yaitu nilai RPN dapat menghasilkan nilai yang identik. Kelemahan tersebut dapat diusulkan dengan pendekatan fuzzy. Pendekatan fuzzy yang digunakan adalah fuzzy inference system (FIS) yang menggunakan konsep Mamdani berdasarkan aturan linguistik melalui tahapan fuzzifikasi, if-then rules, dan defuzzifikasi yang menghasilkan nilai fuzzy RPN (FRPN). Perhitungan FRPN dengan bantuan software MATLAB dapat menentukan prioritas perbaikan pada produk roti. Prioritas perbaikan yang akan dilakukan berada pada peringkat 1-5. Implementasi perbaikan yang dilakukan adalah pelatihan operator mixing dan oven, maintenance mesin forming dan oven, perbaikan jalan yang tidak rata menuju mesin oven, dan peristirahatan mesin forming selama 1 jam setiap pergantian produksi. Selain itu, usulan perbaikan lainnya adalah pemeriksaan mesin forming dan oven setiap per satu minggu dan penggunaan form checksheet untuk memudahkan bagian karyawan maintenance memeriksa serta memperbaiki apabila ditemukan kendala yang terjadi pada mesin di PT. XYZ. Hasil implementasi dapat mengurangi kecacatan produk mencapai 1%.
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Immawan, Taufiq, Wahyudhi Sutrisno, and Annisa Kamilia Rachman. "Operational risk analysis with Fuzzy FMEA (Failure Mode and Effect Analysis) approach (Case study: Optimus Creative Bandung)." MATEC Web of Conferences 154 (2018): 01084. http://dx.doi.org/10.1051/matecconf/201815401084.
Повний текст джерелаАнотація:
Industrial development in Indonesia, manufacturing and services, are required to be able to manage the company very well. However, in practice, the company’s activities are always faced with risks. In general, the risk can be defined as a situation faced by a person or a company in which there is a possibility that harm. The level of risk faced losses due to highly variable depending on the cause and effect influence. To be able to manage (risk management), it can use FMEA (Failure Mode and Effect Analysis). FMEA is a method of analyzing potential failure are applied in product development, system engineering and operational management and is one of a qualitative risk assessment. Using FMEA can also note the value of the RPN (Risk Priority Number) to determine improvement priorities at risk. But there are weaknesses in the use of FMEA, namely RPN calculation is only done by multiplying the severity, occurence and detection alone and irrespective of the degree of importance of each input, to the FMEA method is integrated using fuzzy logic. Fuzzy FMEA is aimed at obtaining the highest fuzzyRPN value which will be used as the focus of improvements to minimize the possibility of these risks occur back. The results were obtained 7 out of 18 types of risks that have a high priority for repairs. Risk troublesome computer (hank / die) while doing photo editing scored the highest RPN 540 (scale 1-1000) and also the highest FRPN 9 (scale 1-10). There is a difference in value between RPN and FRPN. FRPN value obtained from the fuzzification, generate value by taking into account the degree of interest of any given input.
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Li, Zong-Sian, and Kuei-Hu Chang. "A Novel Pythagorean Fuzzy Set–Based Risk-Ranking Method for Handling Human Cognitive Information in Risk-Assessment Problems." Systems 11, no. 8 (August 3, 2023): 402. http://dx.doi.org/10.3390/systems11080402.
Повний текст джерелаАнотація:
With the rapid evolution of the information age and the development of artificial intelligence, processing human cognitive information has become increasingly important. The risk-priority-number (RPN) approach is a natural language-processing method and is the most widely used risk-evaluation tool. However, the typical RPN approach cannot effectively process the various forms of human cognitive information or hesitant information provided by experts in risk assessments. In addition, it cannot process the relative-weight consideration of risk-assessment factors. In order to fully grasp the various forms of human cognitive information provided by experts during risk assessment, this paper proposes a novel Pythagorean fuzzy set–based (PFS) risk-ranking method. This method integrates the PFS and the combined compromise-solution (CoCoSo) method to handle human cognitive information in risk-assessment problems. In the numerical case study, this paper used a healthcare waste-hazards risk-assessment case to verify the validity and rationality of the proposed method for handling risk-assessment issues. The calculation results of the healthcare waste-hazards risk-assessment case are compared with the typical RPN approach, intuitionistic fuzzy set (IFS) method, PFS method, and the CoCoSo method. The numerical simulation verification results prove that the proposed method can comprehensively grasp various forms of cognitive information from experts and consider the relative weight of risk-assessment factors, providing more accurate and reasonable risk-assessment results.
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TAY, KAI MENG, and CHEE PENG LIM. "ON MONOTONIC SUFFICIENT CONDITIONS OF FUZZY INFERENCE SYSTEMS AND THEIR APPLICATIONS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no. 05 (October 2011): 731–57. http://dx.doi.org/10.1142/s0218488511007210.
Повний текст джерелаАнотація:
An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of fuzzy sets and fuzzy rules. In this paper, two useful qualitative properties of the FIS model, i.e., the monotonicity and sub-additivity properties, are studied. The monotonic sufficient conditions of the FIS model with Gaussian membership functions are further analyzed. The aim is to incorporate the sufficient conditions into the FIS modeling process, which serves as a simple (which can be easily understood by domain users), easy-to-use (which can be easily applied to or can be a part of the FIS model), and yet reliable (which has a sound mathematical foundation) method to preserve the monotonicity property of the FIS model. Another aim of this paper is to demonstrate how these additional qualitative information can be exploited and extended to be part of the FIS designing procedure (i.e., for fuzzy sets and fuzzy rules design) via the sufficient conditions (which act as a set of useful governing equations for designing the FIS model). The proposed approach is able to avoid the "trial and error" procedure in obtaining a monotonic FIS model. To assess the applicability of the proposed approach, two practical problems are examined. The first is an FIS-based model for water level control, while the second is an FIS-based Risk Priority Number (RPN) model in Failure Mode and Effect Analysis (FMEA). To further illustrate the importance of the sufficient conditions as the governing equations, an analysis on the consequences of violating the sufficient conditions of the FIS-based RPN model is presented.
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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.
Повний текст джерелаАнотація:
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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Lee, Sung-Hun, Byeong-Chan Oh, and Sung-Yul Kim. "Decision of Maintenance Priority of Power System Using an Improved Risk Priority Number Methodology Based on Fuzzy Theory." TRANSACTION OF THE KOREAN INSTITUTE OF ELECTRICAL ENGINEERS P 70, no. 2 (June 30, 2021): 94–101. http://dx.doi.org/10.5370/kieep.2021.70.2.094.
Повний текст джерела
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Syahputri, Khalida, Jelly Leviza, Rahmi M. Sari, T. Indah Rizkya, Ikhsan Siregar, and Mangara M. Tambunan. "Quality engineering of crumb rubber by minimizing rework using fuzzy FMEA." MATEC Web of Conferences 197 (2018): 14007. http://dx.doi.org/10.1051/matecconf/201819714007.
Повний текст джерелаАнотація:
The quality of crumb rubber has several grades according to Indonesian Rubber Standard (SIR). In crumb rubber process production, lots of products need to rework because it does not match with specified specifications. The types of rework product based on SIR that cause of defects such as hard, soft and dirt. However, the reworked products will produce crumb rubber with lower grade quality. This certainly affects the selling price of the crumb rubber. Also rework will incur additional production costs. Efforts that can be used to minimize the number of rework is to identify the factors that cause failure and determine the priority level of the risk of failure in the production process crumb rubber. It can be used is by using Fuzzy FMEA method. This method aims to get the risk of failure and the level of risk of failure during the crumb rubber production process. Fuzzy FMEA method also obtained the highest RPN value is equal to 489 for the risk of failure of engine dryer temperature is too high. Through the highest priority level of failure (RPN), company can determine the steps to make improvements during the production process to improve the quality of crumb rubber products.
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Cardiel-Ortega, José Jovani, and Roberto Baeza-Serrato. "Failure Mode and Effect Analysis with a Fuzzy Logic Approach." Systems 11, no. 7 (July 7, 2023): 348. http://dx.doi.org/10.3390/systems11070348.
Повний текст джерелаАнотація:
Failure mode and effect analysis (FMEA) is one of the most used techniques in risk management due to its potential to solve multidisciplinary engineering problems. The role of experts is fundamental when developing the FMEA; they identify the failure modes by expressing their opinion based on their experience. A relevant aspect is a way in which the experts evaluate to obtain the indicator of the risk priority number (RPN), which is based on qualitative analysis and a table of criteria where they subjectively and intuitively determine the factor level (severity, occurrence, and detection) for each of the failures. With this, imprecision is present due to the interpretation that each one has regarding the failures. Therefore, this research proposes a fuzzy logic evaluation system with a solid mathematical basis that integrates these conditions of imprecision and uncertainty, thus offering a robust system capable of emulating the evaluation form of experts to support and improve decision making. One of the main contributions of this research is in the defuzzification stage, adjusting the centroid method and treating each set individually. With this, the RPN values approximate to the conventional technique were obtained. Simulations were carried out to test and determine the system’s best structure. The system was validated in a textile company in southern Guanajuato. The results demonstrate that the system reliably represents how experts perform risk assessment.
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Yücenur, G. Nilay, İpek Atay, Senem Argon, and Eda Fulya Gül. "Integrating Fuzzy Prioritization Method and FMEA in the Operational Processes of an Automotive Company." International Journal of Knowledge-Based Organizations 9, no. 3 (July 2019): 14–32. http://dx.doi.org/10.4018/ijkbo.2019070102.
Повний текст джерелаАнотація:
This study includes a risk analysis application. In the article the authors analyzed insurance department's operational processes between October 2015 to April 2016 with a Failure Mode and Effect Analysis (FMEA) of an automotive firm. In the FMEA application, the article summarized engineers and managers opinions during process development and benefitted from these engineers and managers for analysis of these failures one by one. In application study is constructed on data transfer systems analysis among dealer, insurance departments and insurance companies for risk management. The purpose of this study is for the sample company revealing all failures, the reasons for these failures and making a risk analysis of these failures. In this study, for all determined failures was the calculated risk level with the occurrence, severity, and detection score, and preventive functions were organized according to these risk level scores. Using the fuzzy prioritization method for the evaluation of failures, more objective evaluations were attempted and suggestions for the prevention of failures have improved RPN values of 5 failures. In this way, the 5 failures RPN values are lower than 100.
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Lv, Lelin, Huimin Li, Lunyan Wang, Qing Xia, and Li Ji. "Failure Mode and Effect Analysis (FMEA) with Extended MULTIMOORA Method Based on Interval-Valued Intuitionistic Fuzzy Set: Application in Operational Risk Evaluation for Infrastructure." Information 10, no. 10 (October 13, 2019): 313. http://dx.doi.org/10.3390/info10100313.
Повний текст джерелаАнотація:
Failure Mode and Effect Analysis (FMEA) is a useful risk assessment tool used to identify, evaluate, and eliminate potential failure modes in numerous fields to improve security and reliability. Risk evaluation is a crucial step in FMEA and the Risk Priority Number (RPN) is a classical method for risk evaluation. However, the traditional RPN method has deficiencies in evaluation information, risk factor weights, robustness of results, etc. To overcome these shortcomings, this paper aims to develop a new risk evaluation in FMEA method. First, this paper converts linguistic evaluation information into corresponding interval-valued intuitionistic fuzzy numbers (IVIFNs) to effectively address the uncertainty and vagueness of the information. Next, different priorities are assigned to experts using the interval-valued intuitionistic fuzzy priority weight average (IVIFPWA) operator to solve the problem of expert weight. Then, the weights of risk factors are subjectively and objectively determined using the expert evaluation method and the deviation maximization model method. Finally, the paper innovatively introduces the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator, Tchebycheff Metric distance, and the interval-valued intuitionistic fuzzy weighted geometric (IVIFWG) operator into the ratio system, the reference point method, and the full multiplication form of MULTIMOORA sub-methods to optimize the information aggregation process of FMEA. The extended IVIF-MULTIMOORA method is proposed to obtain the risk ranking order of failure modes, which will help in obtaining more reasonable and practical results and in improving the robustness of results. The case of the Middle Route of the South-to-North Water Diversion Project’s operation risk is used to demonstrate the application and effectiveness of the proposed FMEA framework.
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Kushwaha, Dinesh Kumar, Dilbagh Panchal, and Anish Sachdeva. "Risk analysis of cutting system under intuitionistic fuzzy environment." Reports in Mechanical Engineering 1, no. 1 (December 3, 2020): 162–73. http://dx.doi.org/10.31181/rme200101162k.
Повний текст джерелаАнотація:
Failure Mode Effect Analysis (FMEA) is popular and versatile approach applicable to risk assessment and safety improvement of a repairable engineering system. This method encompasses various fields such as manufacturing, healthcare, paper mill, thermal power industry, software industry, services, security etc. in terms of its application. In general, FMEA is based on Risk Priority Number (RPN) score which is found by product of probability of Occurrence (O), Severity of failure (S) and Failure Detection (D). As human judgement is approximate in nature, the accuracy of data obtained from FMEA members depend on degree of subjectivity. The subjective knowledge of members not only contains uncertainty but hesitation too which in turn, affect the results. Fuzzy FMEA considers uncertainty and vagueness of the data/ information obtained from experts. In order to take into account, the hesitation of experts and vague concept, in the present work we propose integrated framework based on Intuitionistic Fuzzy- Failure Mode Effect Analysis (IF-FMEA) and IF-Technique for Order Preference by Similarity to Ideal Solution (IF-TOPSIS) techniques to rank the listed failure causes. Failure cause Fibrizer (FR) was found to be the most critical failure cause with RPN score 0.500. IF-TOPSIS has been implemented within IF-FMEA to compare and verify ranking results obtained by both the IF based approaches. The proposed method was presented with its application for examining the risk assessment of cutting system in sugar mill industry situated in western Uttar Pradesh province of India. The result would be useful for the plant maintenance manager to fix the best maintenance schedule for improving availability of cutting system.
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Mangla, Sachin Kumar, Sunil Luthra, and Suresh Jakhar. "Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA." Benchmarking: An International Journal 25, no. 8 (November 29, 2018): 2660–87. http://dx.doi.org/10.1108/bij-04-2017-0074.
Повний текст джерелаАнотація:
PurposeThe purpose of this paper is to facilitate green supply chain (GSC) managers and planners to model and access GSC risks and probable failures. This paper proposes to use the fuzzy failure mode and effects analysis (FMEA) approach for assessing the risks associated with GSC for benchmarking the performance in terms of effective GSC management adoption and sustainable production.Design/methodology/approachInitially, different failure modes are defined using FMEA analysis, and in order to decide the risk priority, the risk priority number (RPN) is determined. Such priority numbers are typically acquired from the judgment decisions of experts that could contain the element of vagueness and imperfection due to human biases, and it may lead to inaccuracy in the process of risk assessment in GSC. In this study, fuzzy logic is applied to conventional FMEA to overcome the issues in assigning RPNs. A plastic manufacturer GSC case exemplar of the proposed model is illustrated to present the authenticity of this method of risk assessment.FindingsResults indicate that the failure modes, given as improper green operating procedure, i.e. process, operations, etc. (R6), and green issues while closing the loop of GSC (R14) hold the highest RPN and FRPN scores in classical as well as fuzzy FMEA analysis.Originality/valueThe present research work attempts to propose an evaluation framework for risk assessment in GSC. This paper explores both sustainable developments and risks related to efficient management of GSC initiatives in a plastic industry supply chain context. From a managerial perspective, suggestions are also provided with respect to each failure mode.
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Kumar, Mahendra. "Risk Management in Road Construction Projects using Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3759–66. http://dx.doi.org/10.22214/ijraset.2021.35805.
Повний текст джерелаАнотація:
This research presents a complete framework for managing risks in road construction projects using Fuzzy-Risk Failure mode & Effect Analysis (F-RFMEA) system. In this research risk management is done through undergoing five major steps: 1. Identification of risk factors through literature review and discussion with highway construction expert’s result in 61 risk factors are identified, which are categorized in 12 groups 2. Assessment of risk factors in terms of its occurrence, consequences on cost, time & quality and detectability through questionnaire survey. 3. Ranking of risk factors based on Risk Priority Number (RPN, function of occurrence, consequence and detectability of risk) using Fuzzy-MATLAB 4. Risk Allocation i.e. to whom risk should be allocated 5.Treating the risks by designing risk response strategies.
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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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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 2, no. 2 (August 1, 2018): 6–21. http://dx.doi.org/10.2478/ejef-2018-0007.
Повний текст джерелаАнотація:
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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baghbani, Mohammad, Soleyman Iranzadeh, and Majid Bagherzadeh khajeh. "Investigating the relationship between RPN parameters in fuzzy PFMEA and OEE in a sugar factory." Journal of Loss Prevention in the Process Industries 60 (July 2019): 221–32. http://dx.doi.org/10.1016/j.jlp.2019.05.003.
Повний текст джерела
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Giardina, Mariarosa, Elio Tomarchio, Pietro Buffa, Maurizio Palagonia, Ivan Veronese, and Marie Claire Cantone. "FMECA Application in Tomotherapy: Comparison between Classic and Fuzzy Methodologies." Environments 9, no. 4 (April 10, 2022): 50. http://dx.doi.org/10.3390/environments9040050.
Повний текст джерелаАнотація:
Accident analysis in radiotherapy highlighted the need to increase quality assurance (QA) programs by the identification of failures/errors with very low probability (rare event) but very severe consequences. In this field, a Failure Mode, Effects and Criticality Analysis (FMECA) technique, used in various industrial processes to rank critical events, has been met with much interest. The literature describes different FMECA methods; however, it is necessary to understand if these tools are incisive and effective in the healthcare sector. In this work, comparisons of FMECA methodologies in the risk assessment of patients undergoing treatments performed with helical tomotherapy are reported. Failure modes identified for the phases “treatment planning” and “treatment execution” are classified using the Risk Priority Number (RPN) index. Differences and similarities in the classification of failures/errors of the examined FMECA approaches are highlighted.
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Fattahi, Reza, Reza Tavakkoli-Moghaddam, Mohammad Khalilzadeh, Nasser Shahsavari-Pour, and Roya Soltani. "A novel FMEA model based on fuzzy multiple-criteria decision-making methods for risk assessment." Journal of Enterprise Information Management 33, no. 5 (June 4, 2020): 881–904. http://dx.doi.org/10.1108/jeim-09-2019-0282.
Повний текст джерелаАнотація:
PurposeRisk assessment is a very important step toward managing risks in various organizations and industries. One of the most extensively applied risk assessment techniques is failure mode and effects analysis (FMEA). In this paper, a novel fuzzy multiple-criteria decision-making (MCDM)-based FMEA model is proposed for assessing the risks of different failure modes more accurately.Design/methodology/approachIn this model, the weight of each failure mode is considered instead of risk priority number (RPN). Additionally, three criteria of time, cost and profit are added to the three previous risk factors of occurrence (O), severity (S) and detection (D). Furthermore, the weights of the mentioned criteria and the priority weights of the decision-makers calculated by modified fuzzy AHP and fuzzy weighted MULTIMOORA methods, respectively, are considered in the proposed model. A new ranking method of fuzzy numbers is also utilized in both proposed fuzzy MCDM methods.FindingsTo show the capability and usefulness of the suggested fuzzy MCDM-based FMEA model, Kerman Steel Industries Factory is considered as a case study. Moreover, a sensitivity analysis is conducted for validating the achieved results. Findings indicate that the proposed model is a beneficial and applicable tool for risk assessment.Originality/valueTo the best of authors’ knowledge, no research has considered the weights of failure modes, the weights of risk factors and the priority weights of decision-makers simultaneously in the FMEA method.
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Chang, Kuei-Hu. "A New Emergency-Risk-Evaluation Approach under Spherical Fuzzy-Information Environments." Axioms 11, no. 9 (September 16, 2022): 474. http://dx.doi.org/10.3390/axioms11090474.
Повний текст джерелаАнотація:
When major emergencies or accidents occur, risk evaluation and prediction are the most important means to reduce their impact. Typical risk evaluation uses the failure mode and effects analysis (FMEA) method for failure-risk ranking and control. However, when faced with severe special infectious diseases such as COVID-19, there are many cognitive and information uncertainties that the FMEA method is unable to effectively handle. To effectively deal with the issue of risk evaluation when major emergencies or accidents occur, this paper integrated the risk-priority number and spherical fuzzy-sets methods to propose a novel emergency-risk-evaluation method. In the numerical verification, this paper applied the example of preventing secondary COVID-19 transmissions in hospitals to explain the calculation procedure and validity of the proposed new emergency-risk-evaluation approach. The calculation results were also compared with the typical RPN, fuzzy-set, and intuitionistic fuzzy-set methods. The calculation results showed that the proposed new emergency-risk-evaluation approach could effectively handle the cognitive and informational uncertainties of emergency-risk-evaluation issues during the COVID-19 pandemic.
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Kadena, Esmeralda, Sinan Koçak, Katalin Takács-György, and András Keszthelyi. "FMEA in Smartphones: A Fuzzy Approach." Mathematics 10, no. 3 (February 5, 2022): 513. http://dx.doi.org/10.3390/math10030513.
Повний текст джерелаАнотація:
Smartphones are attracting increasing interest due to how they are revolutionizing our lives. On the other hand, hardware and software failures that occur in them are continually present. This work aims to investigate these failures in a typical smartphone by collecting data from a class of people. Concerns have been raised that call into question the efficiency of applied methods for identifying and prioritizing the potential defects. The widely used hybridized engineering method, Fuzzy Failure Mode and Effect Analysis (F-FMEA), is an excellent approach to solving these problems. The F-FMEA method was applied to prioritize the potential failures based on their Severity (S), expected Occurrence (O), and the likelihood of Detectability (D). After collecting failure data from different users on a selected smartphone, two well-known defuzzification methods facing the Risk Priority Number (RPN) in F-FMEA were applied. Despite this interest, to the best of our knowledge, no one has studied smartphone failures with a technique that combines the results of different fuzzy applications. Thus, to combine the results of the derived fuzzy subsystems for the average value, we suggest a summative defuzzification method. Our findings indicate that F-FMEA with a summative defuzzification procedure is a clear improvement on the F-FMEA method. Even though the summation method modifies close results of the defuzzification one, it was shown that it provides more accurate results.
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., Malkhan. "Fuzzy Logic Based Approach for Analysis and Synthesis of Failure Modes." Journal of Advanced Research in Production and Industrial Engineering 8, no. 1&2 (April 19, 2021): 18–22. http://dx.doi.org/10.24321/2456.429x.202102.
Повний текст джерелаАнотація:
Failure analysis and management has been gaining a lot of importance for the last few years. Failure mode and effect analysis is the process used for failure analysis and risk management in judging various businesses and managements. The FMEA is the methodology extensively used in different sectors to detect, prioritize and investigate the potential failures which occur during the production or design of the product. This whole process is computed with the Risk Priority Number (RPN). The blurriness and uncertainty in the FMEA associated with the human expertise and experience is responded using Fuzzy Logic based approach. In today’s time, organizations mainly focus on improving the quality of their product and building the reputation of their organizations. So, they consider every aspect of failure which can occur during the production. So, they use the FMEA team to overcome these issues before their existence. This paper is based on the approach of FMEA methodology and Fuzzy logic which describes multiple risks, insecurities associated with the different production processes and gives the solution to overcome the possible failures. FMEA is a methodology which suggests that such type of failures could occur during the design of the product or the production of the product.
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Waluny, Arum, and Endang Suhendar. "Analisis Risiko Kegagalan Proses Menggunakan FuzzyAHP, FMEA dan Kaizen Method Pada PT. Central Mega Kencana." Jurnal Teknologi dan Manajemen 21, no. 1 (March 1, 2023): 9–24. http://dx.doi.org/10.52330/jtm.v21i1.72.
Повний текст джерелаАнотація:
Setiap perusahaan menginginkan proses produksinya mendapatkan produk yang bermutu baik dari segi proses produksi yang efektif dan efisien serta bisa memberikan kepuasan kepada konsumen. Perusahaan harus melakukan peningkatan kualitas dari setiap produk yang dihasilkan dan menekan jumlah kecacatan yang ada. Salah satu cara yang bisa dilakukan ialah menganalisa resiko produksi demi menjamin keberhasilan suatu produksi dan bisnis. Tujuan dilakukan penelitian ini adalah menganalisa risiko penyebab terhambatnya proses produksi, mengidentifikasi faktor-faktor yang menyebabkan terjadinya risiko terhambatnya proses produksi, dan strategi untuk meminimalkan risiko yang terjadi pada produksi perhiasan PT. Central Mega Kencana. Melihat hal tersebutperlu dilakukan suatu sistem manajemen risiko yang mana dalam mengidentifikasi dan menyelesaian masalah manajemen risiko ini ada beberapa metode yang dapat digunakan antara lain yaitu metode Failure Mode and Effect Analysis (FMEA), Fuzzy AHP, dan juga Kaizen Method. Dalam penelitian ini, hasil dari pengolahan data yang dilakukan yaitu, didapatkan hasil pada proses produksi PT. Central Mega Kencana yang mendapat nilai RPN tertinggi pada Proses Rakit dengan RPN pada dua mode kegagalan sebesar 737,107 dan 613,783. Untuk kemudian dua mode kegagalan tersebut diprioritaskan untuk diberikan tindakan perbaikan. Usulan tindakan perbaikan yang dilakukan yaitu dengan penerapan Kaizen melalui pendekatan PDCA.
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Zhang, Shu Zhong, and Qin Da Zeng. "Risk Evaluation of Failure Mode Using Fuzzy AHP and Variable Weight Synthesizing." Advanced Materials Research 566 (September 2012): 414–19. http://dx.doi.org/10.4028/www.scientific.net/amr.566.414.
Повний текст джерелаАнотація:
Aiming at the most critically debated disadvantage of risk evaluation of failure mode using traditional RPN (Risk Priority Number), that the degree of failure severity isn’t accurately evaluated and the three risk factors are assumed to be equally important. The method, risk evaluation of failure mode using fuzzy Analytic Hierarchy Process (AHP) and variable weight synthesizing is put forward. The model is separated into two layers. Firstly, according to the production loss, repair costs, safety affection and environment impact, fuzzy mathematics, AHP and encouragement variable weight synthesizing are introduced into the quantitative evaluation of failure severity to improve its accuracy and objectiveness. Then, via severity, occurrence, and detection, encouragement variable weight and AHP are used for failure mode risk evaluation to make evaluation results closer to the reality. At last, an example is provided to illustrate the potential applications of the proposed model and the detailed computational process is presented. And the result shows the accuracy and objectiveness can be improved by the proposed model.
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Pan, Guangze, Dan Li, Qian Li, Yaqiu Li, and Yuanhang Wang. "Advanced FMECA Method Based on Intuitionistic 2-Tuple Linguistic Variables and the Triangular Fuzzy Analytic Hierarchy Process." Wireless Communications and Mobile Computing 2022 (June 6, 2022): 1–7. http://dx.doi.org/10.1155/2022/1495934.
Повний текст джерелаАнотація:
Failure mode effects and criticality analysis (FMECA) is a commonly adopted approach to defining, assessing, and reducing possible failures in designs, systems, processes, products, and services. Traditional FMECA ranks the failure modes of products based on a risk priority number (RPN), which is obtained by multiplying the risk elements. Conventional FMECA has the shortcomings of badly handling unknown information and unreasonably assessing RPNs. To deal with these issues, an advanced FMECA method based on intuitionistic 2-tuple linguistic variables (I2LVs) and the triangular fuzzy analytic hierarchy process (TFAHP) is proposed. In this method, the fuzzy evaluation of risk elements given by different FMECA members is represented by I2LVs, which can efficiently handle unknown information. The TFAHP method is adopted to assess the weights of risky elements and rank the risk priorities of different failure modes. Finally, an application case of an insulated-gate bipolar transistor is used to verify the effectiveness and robustness of the proposed method.
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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.
Повний текст джерелаАнотація:
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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Cruz-Rivero, Lidilia, María Leonor Méndez-Hernández, Carlos Eusebio Mar-Orozco, Alberto A. Aguilar-Lasserre, Alfonso Barbosa-Moreno, and Josué Sánchez-Escobar. "Functional Evaluation Using Fuzzy FMEA for a Non-Invasive Measurer for Methane and Carbone Dioxide." Symmetry 14, no. 2 (February 20, 2022): 421. http://dx.doi.org/10.3390/sym14020421.
Повний текст джерелаАнотація:
This paper combines the use of two tools: Failure Mode and Effect Analysis (FMEA) and Fuzzy Logic (FL), to evaluate the functionality of a quantifier prototype of Methane gas (CH4) and Carbon Dioxide (CO2), developed specifically to measure the emissions generated by cattle. Unlike previously reported models for the same purpose, this device reduces damage to the integrity of the animal and does not interfere with the activities of livestock in their development medium. FMEA and FL are used to validate the device’s functionality, which involves identifying possible failure modes that represent a more significant impact on the operation and prevent the prototype from fulfilling the function for which it was created. As a result, this document presents the development of an intelligent fuzzy system type Mamdani, supported in the Fuzzy Inference System Toolbox of MatLabR2018b®, for generating a risk priority index. A Fuzzy FMEA model was obtained to validate the prototype for measuring Methane and Carbon Dioxide emissions, which allows considering this prototype as a reliable alternative for the reliable measurement of these gases. This study was necessary as a complementary part in the validation of the design of the prototype quantifier of CH4 and CO2 emissions. The methods used (classic FMEA and Fuzzy FMEA) to evaluate the RPN show asymmetric graphs due to data disparity. Values in the classical method are mostly lower than the Mamdani model results due to the description of the criteria with which it is evaluated.
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Krisnaningsih, Erni, Arif Budi Sulistyo, Abdul Rahim, and Saleh Dwiyatno. "Fuzzy risk priority number assessment to detect midsole product defects." Jurnal Sistem dan Manajemen Industri 6, no. 1 (June 30, 2022): 77–88. http://dx.doi.org/10.30656/jsmi.v6i1.4013.
Повний текст джерелаАнотація:
The quality of midsole products significantly impacts the quality, durability, and comfort of the users of shoe products manufactured by ABC company. The high percentage of the average number of product defects based on product yield data for 12 months is 3.1% which exceeds the average number of product defects required by the company by 2%. There are six types of defects in the midsole: yellowing, porous, bubbles, broken, over left material and trimming. Three types of midsole product defects from six types of defects are the focus of improvement based on the Pareto concept, the name of the type of defect is yellowing. Porous and over left material with a total defect percentage of 82.9%. This study aims to evaluate flaws in the midsole production process using a method that combines failure mode and effect analysis (FMEA), fuzzy logic, and Pareto diagram analysis. FMEA is used in shoe manufacturing to identify failure modes, their causes, and their effects. In contrast, fuzzy logic methods for input factors, such as occurrence (O), severity (S), and detection (D), are used to obtain a fuzzy risk priority number (FRPN). The assessment using rule-based FRPN provides strong evidence that the proposed methodology is logically useful for prioritizing the value of the RPN.
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Zhou, Bin, Jing Chen, Qun Wu, Dragan Pamučar, Weizhong Wang, and Ligang Zhou. "RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT." Facta Universitatis, Series: Mechanical Engineering 20, no. 2 (July 28, 2022): 399. http://dx.doi.org/10.22190/fume220223013z.
Повний текст джерелаАнотація:
The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach.
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Hameed, Abdul Zubar, Jayakrishna Kandasamy, Sakthivel Aravind Raj, Majed Abubakr Baghdadi, and Muhammad Atif Shahzad. "Sustainable Product Development Using FMEA ECQFD TRIZ and Fuzzy TOPSIS." Sustainability 14, no. 21 (November 2, 2022): 14345. http://dx.doi.org/10.3390/su142114345.
Повний текст джерелаАнотація:
Increasing demand for simplification of frequently used products promotes innovation with simple mechanisms. In this study, an attempt is made by amalgamating FMEA, QFD, TRIZ, LCA, and fuzzy TOPSIS for the development of sustainable products. The methodology is executed to redesign a simple pressure relief valve (PRV) in an economic, sustainable, and innovative manner. The failure modes of PRV are considered in FMEA and ranked by their RPN. The environmental voice of the customer is imbibed in ECQFD to correlate with the engineering specifications and obtain the critical zones of improvement. Possible innovations of the existing design based on the design option selected from ECQFD are considered by analyzing the design feature contradictions, and solutions are addressed using the TRIZ matrix. Four CAD models of improved designs were created and subjected to sustainability analysis and fatigue life analysis. The redesigns were evaluated using fuzzy TOPSIS, an MCDM approach, to obtain an unbiased ranking based on multiple criteria supported by the result analysis and expert opinions. The final design was selected for prototyping. This approach provides a holistic approach for innovative product design and development and a simplified approach for an innovative redesign of an existing product.
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Islam, Sri Susilawati. "Analisis Preventive Maintenance Pada Mesin Produksi dengan Metode Fuzzy FMEA." JTT (Jurnal Teknologi Terpadu) 8, no. 1 (April 27, 2020): 13–20. http://dx.doi.org/10.32487/jtt.v8i1.766.
Повний текст джерелаАнотація:
Kegiatan perawatan mesin (maintenance) merupakan kegiatan yang memiliki peran yang sangat penting untuk menunjang kelancaran proses produksi. Maintenance yang tidak teratur dapat mengakibatkan mesin mengalami gangguan atau kerusakan (downtime), hal ini dapat menghambat kelancaran proses produksi. Agar proses produksi dapat berjalan sesuai dengan perencanaan, maka harus ditunjang dengan keadaan mesin yang handal sehingga diperlukan perencanaan perawatan mesin yang baik. Pereventive maintenance merupakan salah satu perencanaan perawatan yang banyak digunakan untuk memastikan bahwa mesin dapat berjalan sesuai dengan fungsinya. Pada penelitian ini, studi kasus dilakukan di PT PLN Sektor Tello Makassar sebab perusahaan tersebut mempunyai peranan penting dalam menyuplai arus listrik di kota Makassar yaitu sebesar 197,61 MW. Jika terjadi kerusakan mesin maka akan berdampak pada aktivitas ekonomi masyarakat yang dapat menyebabkan kerugian, saat ini perencanaan maintenance yang dilakukan oleh PT PLN Sektor Tello Makassar belum berjalan optimal karena masih terjadinya kerusakan mesin generator saat proses produksi berlangsung. Agar kegagalan mesin dapat dikurangi, maka penelitian ini akan menggunakan metode Fuzzy FMEA (Failure Mode and Effect Analysis) untuk mengidentifikasi kerusakan mesin dan menentukan prioritas mesin dalam proses perawatan serta jenis perawatannya berdasarkan nilai RPN (Risk Priority Number).
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Septiyana, Diah. "FUZZY FMEA APPLICATION TO IDENTIFICATION RISK IN-PROCESS PRODUCTION OF TOYOTA HI-ACE WIRING HARNESS PRODUCT." J@ti Undip: Jurnal Teknik Industri 16, no. 3 (October 1, 2021): 153–60. http://dx.doi.org/10.14710/jati.16.3.153-160.
Повний текст джерелаАнотація:
In product manufacture, the high failure rate problem of produce product is the number of product defects. Several types of defects have a high enough percentage. To solve this problem, we need to identify the failures and to get the assessment information of the three risk factors. Our research using the traditional FMEA method at the production of Wiring Harness products to shows the current condition of various modes of failure in those areas. This study focuses on implementing fuzzy FMEA to identify the potential risks that may occur along with the assembling of the Wiring Harness process. The fuzzy FMEA approach is preventing product and process problems before they occur, this paper is also expected to result in some mitigation effort that can be applied to improve the Wiring Harness production process. With the Fuzzy FMEA method, we have found the highest FRPN value that shows the highest defect such as damage insulation is 8.5, damage terminal is 8.5, and the damaged part is 8.5 and the highest RPN from the traditional FMEA is damage insulation (324). To solve this problem, we propose to use the fishbone diagram and give suggestions for improvements to the highest failure modes that are damaged insulation.
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Li, Huimin, Li Ji, Feng Li, Hairui Li, Qingguo Sun, Zhihong Li, Hongmei Yan, Wei Guan, Lunyan Wang, and Ying Ma. "Operational Safety Risk Assessment for the Water Channels of the South-to-North Water Diversion Project Based on TODIM-FMEA." Complexity 2020 (December 12, 2020): 1–15. http://dx.doi.org/10.1155/2020/6691764.
Повний текст джерелаАнотація:
The South-to-North Water Diversion Project consists of long-distance water delivery channels and a complicated geological environment along the way. To deal with the operation safety of the water conveyance channels in the middle route of the South-to-North Water Diversion Project, this study analyzes six failure modes: structural cracks, poor water delivery during ice periods, instability of canal slopes, material aging, abnormal leakage, and foundation defects. Based on FMEA, a multigranularity language evaluation method that can be converted into interval intuitionistic fuzzy numbers is used to evaluate the severity (S), occurrence (O), and detection difficulty (D) of the six failure modes. Interval intuitionistic fuzzy entropy is used to calculate the weights of the risk factors. Finally, a ranking model of each failure mode is built based on the TODIM method. The final ranking results show that the risk of abnormal leakage is the largest, and the risk of poor water delivery during ice periods is the smallest. The feasibility and validity of the calculation results are verified by comparing them with the ranking results of the traditional RPN and TOPSIS methods. The TODIM-FMEA risk assessment model offers a new solution to the problem of risk assessment for water transfer projects.
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Li, Qiang, Liyang Xie, Jiaxin Song, Haiyang Li, and Guoliang Xu. "Research Methods and Applications of Gear Manufacturing Process Optimization." Mathematical Problems in Engineering 2019 (May 28, 2019): 1–17. http://dx.doi.org/10.1155/2019/7043604.
Повний текст джерелаАнотація:
This paper presents an optimization method for gear processes; through this method, the most worth optimized processes can be obtained and optimized, thus improving the reliability and supportability of gear products. Firstly, the POPN (Process Optimization Priority Number) analysis method considering the current process level and the process improvement cost is proposed with reference to the RPN (Risk Priority Number) analysis method to obtain the most worth optimized processes. Due to the fact that the unreasonable weight distribution of importance (I), changing difficulty (C), rationality (R), and detective difficulty (D) still exists in the POPN analysis, then combining the POPN analysis method and the AHP (Analytic Hierarchy Process)-fuzzy comprehensive evaluation method to evaluate and rank the FPOPN (fuzzy POPN) level of each process, the higher the FPOPN level is, the more the process should be optimized. Finally, according to the evaluation results, some processes with high FPOPN level are optimized and the optimal parameters’ combination of these processes is obtained through the tests based on the Taguchi method. A detailed optimization example is also given from the beginning to end in accordance with the above methods, and compared with the original process gears, the optimized gears have a big increase in gear performance through the above optimization method.
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Chang, Kuei-Hu. "Integrating Spherical Fuzzy Sets and the Objective Weights Consideration of Risk Factors for Handling Risk-Ranking Issues." Applied Sciences 13, no. 7 (April 2, 2023): 4503. http://dx.doi.org/10.3390/app13074503.
Повний текст джерелаАнотація:
Risk assessments and risk prioritizations are crucial aspects of new product design before a product is launched into the market. Risk-ranking issues involve the information that is considered for the evaluation and objective weighting considerations of the evaluation factors that are presented by the data. However, typical risk-ranking methods cannot effectively grasp a comprehensive evaluation of this information and ignore the objective weight considerations of the risk factors, leading to inappropriate evaluation results. For a more accurate ranking result of the failure mode risk, this study proposes a novel, flexible risk-ranking approach that integrates spherical fuzzy sets and the objective weight considerations of the risk factors to process the risk-ranking issues. In the numerical case validation, a new product design risk assessment of electronic equipment was used as a numerically validated case, and the simulation results were compared with the risk priority number (RPN) method, improved risk priority number (IRPN) method, intuitionistic fuzzy weighted average (IFWA) method, and spherical weighted arithmetic average (SWAA) method. The test outcomes that were confirmed showed that the proposed novel, flexible risk-ranking approach could effectively grasp the comprehensive evaluation information and provide a more accurate ranking of the failure mode risk.
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Lian, Xiaozhen, Liang Hou, Wenbo Zhang, Xiangjian Bu, and Huasheng Yan. "An Integrated Approach for Failure Mode and Effects Analysis Based on Weight of Risk Factors and Fuzzy PROMETHEE Ⅱ." Symmetry 14, no. 6 (June 9, 2022): 1196. http://dx.doi.org/10.3390/sym14061196.
Повний текст джерелаАнотація:
Design experts need to fully understand the failure risk of a product to improve its quality and reliability. However, design experts have different understandings of and concepts in the risk evaluation process, which will lead to cognitive asymmetry in the product’s redesign. This phenomenon of cognitive asymmetry prevents experts from improving the reliability of a product, increasing the risk of product development failure. Traditionally, failure mode and effects analysis (FMEA) has been widely used to identify the failure risk in redesigning products and a system’s process. The risk priority number (RPN), which is determined by the risk factors (RF), namely, the occurrence (O), severity (S), and detection (D), is the index used to determine the priority ranking of the failure modes (FM). However, the uncertainty about the evaluation information for the RF and the coupling relationship within the FM have not been taken into account jointly. This paper presents an integrated approach for FMEA based on an interval-valued intuitionistic fuzzy set (IVIFS), a fuzzy information entropy, a non-linear programming model, and fuzzy PROMETHEE Ⅱ to solve the problem of cognitive asymmetry between experts in the risk evaluation process. The conclusions are as follows: Firstly, an IVIFS is used to present the experts’ evaluation information of the RF with uncertainty, and the fuzzy information entropy is utilized to obtain the weight of the experts to integrate the collective decision matrix. Secondly, a simplified non-linear programming model is utilized to obtain the weight of the RF to derive the weighted preference index of the FM. Subsequently, the coupling relationship within the FM is estimated by fuzzy PROMETHEE Ⅱ, where the net flow is given to estimate the priority ranking of the FM. Finally, the proposed approach is elaborated on using a real-world case of a liquid crystal display. Methods comparison and sensitivity analyses are conducted to demonstrate the validity and feasibility of the proposed approach.
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Chakhrit, A., M. Bougofa, I. H. M. Guetarni, N. Nehal, A. Bouafia, F. Z. Ghazli, R. Kharzi, and M. Chennoufi. "New risk assessment and prioritization failure modes based approach in a gas turbine system." IOP Conference Series: Earth and Environmental Science 1098, no. 1 (October 1, 2022): 012009. http://dx.doi.org/10.1088/1755-1315/1098/1/012009.
Повний текст джерелаАнотація:
Abstract The dependability occupies a strong place in the performance achievement of the system. It describes the mechanisms that lead to failures of systems. Failure mode and effects, analysis (FMEA) is a classical safety technique widely used in several safety critical industries. This method uses the risk priority number (RPN) to assess the criticality value and prioritize failure modes. However, it suffers from some drawbacks regarding the situation where the in-formation provided is ambiguous or uncertain. Thus, in this work, a fuzzy criticality assessment based approach is carried out to evaluate the failure modes of the relevant system and gives an alternate prioritizing to that obtained by the conventional method. In addition, a novel hybrid approach is proposed that combines the grey relational approach (GRA) and fuzzy analytic hierarchy process. This approach offers a new ranking of failure modes by solving the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection, and frequency, which are considered to have equal importance in the traditional method. A real case study from a gas turbine system provides encouraging results regarding the risk evaluation and prioritizing failures mode with handling different forms of ambiguity, uncertainty, and divergent judgments of experts.
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Chen, Tzu-Chia, Jiuqi Yu, and Amir-Mohammad Golmohammadi. "Application of Sustainable Education Innovation in the Integrated Teaching of Theory and Practice Adopted in the Auto Chassis Course." Discrete Dynamics in Nature and Society 2022 (June 24, 2022): 1–16. http://dx.doi.org/10.1155/2022/7737231.
Повний текст джерелаАнотація:
With the swift advancement of the auto repair industry, the demand for relevant talents and professionals in auto repair is thereby increasing steadily. Meanwhile, due to higher and higher expectations and requirements from contemporary society towards these professionals, it is a must to continuously reform the course teaching mode adopted for the auto repair specialists in the secondary vocational schools to adapt to the market needs ultimately. The study’s primary aim was to investigate the application of sustainable education innovation in the integrated teaching of theory and practice adopted in the auto chassis course. Some research tools such as fuzzy Delphi method, fuzzy failure mode and effect analysis (FFMEA), and theory of inventive problem-solving (TRIZ) have been employed during the research process. After surveying and collecting the security incidents, which occurred in the integrated teaching of theory and practice adopted in the automobile chassis course, the presented reality that teachers were reluctant to accept the integrated teaching of theory and practice, and the deficiencies sensed in the school-enterprise engagement; the researcher firstly obtained the risk priority number (RPN) of each deficiency above via fuzzy failure mode and effect analysis (FFMEA) and accordingly expounded how TRIZ was applied under this circumstance. Furthermore, the conflicts among the enterprise, school, teachers, and students were explored in sequence utilizing technical contradiction solutions. Henceforth, three strategies were generated and proposed aimed at enhancing the integrated course teaching of theory and practice for the automobile chassis major: (1) hardware construction, involving the construction of not only the teaching workplace and teaching equipment but also teaching faculty training; (2) resource construction, along with the construction of school-based courses; and (3) improvement of classroom teaching. At the last stage, the three advancement strategies' validity was verified based on the implementation of a six-month remedial period.
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Pang, Jihong, Jinkun Dai, and Faqun Qi. "A Potential Failure Mode and Effect Analysis Method of Electromagnet Based on Intuitionistic Fuzzy Number in Manufacturing Systems." Mathematical Problems in Engineering 2021 (August 23, 2021): 1–12. http://dx.doi.org/10.1155/2021/9998526.
Повний текст джерелаАнотація:
Failure mode and effect analysis (FMEA) is a systematic activity in the stage of product design and process design. However, the traditional FMEA has some shortcomings in practical application, such as too many evaluation languages, uncertain weights of influencing factors, and uncertain weights of evaluation members. This paper presents an FMEA evaluation method in manufacturing system based on similarity measure, nonlinear programming model, and intuitionistic fuzzy number (IFN). Firstly, the IFN is used to evaluate failure mode, which overcomes the defect of traditional FMEA evaluation value. Secondly, the weight of failure evaluation team members is solved according to the concept of similarity measure to make up for the blank of evaluation members’ weight aiming at the shortage of unknown weight. Then, the definition of consensus measure is introduced to make the evaluators reach a consensus, and the weights of influencing factors of failure modes (FMs) are calculated. Finally, the weights of evaluators and influencing factors are calculated by IFN algorithm and score function, and the score value of each FM is obtained to rank instead of risk priority number (RPN). The objectivity and practicability of the new method are verified by the example of failure mode for an attractive electromagnet manufacturing system.