scholarly journals Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method

Mathematics ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 536 ◽  
Author(s):  
Jianghong Zhu ◽  
Bin Shuai ◽  
Rui Wang ◽  
Kwai-Sang Chin
Keyword(s):  
Risk Assessment ◽  
Failure Mode ◽  
Failure Modes ◽  
Bonferroni Mean ◽  
Psychological Behavior ◽  
Analysis Technique ◽  
Safety And Reliability ◽  
Preference Interdependence ◽  
Fmea Method ◽  
Grey Relational

As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This study aims at presenting a comprehensive FMEA model that could efficiently handle the preference interdependence and psychological behavior of experts in the process of failure modes ranking. In this model, a linguistic variable expressed by the interval-valued Pythagorean fuzzy number (IVPFN) is utilized by experts to provide preference information with regard to failure modes’ evaluation and risk factors’ weight. Then, to depict the interdependent relationships between experts’ preferences, the Bonferroni mean operator is extended to IVPFN to aggregate the experts’ preference. Subsequently, an extended TODIM approach in which the dominance degree of failure modes is calculated by grey relational analysis is utilized to determine the risk priority of failure modes. Finally, a practical example concerning the risk assessment of a nuclear reheat valve system is provided to demonstrate the effectiveness and feasibility of the presented method. In addition, a sensitivity analysis and comparison analysis are conducted, and the results show that the preference interdependence and psychological behavior of experts have an important effect on the risk priority of failure modes.

Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA

2018 ◽  
Vol 25 (8) ◽  
pp. 2660-2687 ◽  
Author(s):  
Sachin Kumar Mangla ◽  
Sunil Luthra ◽  
Suresh Jakhar
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Failure Mode ◽  
Failure Modes ◽  
Research Work ◽  
Evaluation Framework ◽  
Green Supply Chain ◽  
Content Type ◽  
Efficient Management ◽  
Fmea Analysis

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.

Fuzzy Hybrid FMEA for Risk Assessment in Service Industry

2021 ◽  
pp. 43-75
Author(s):  
Nihan Kabadayi
Keyword(s):  
Risk Assessment ◽  
Hospitality Industry ◽  
Failure Modes ◽  
Service Industry ◽  
Service Providers ◽  
Real Life ◽  
Potential Risks ◽  
Fmea Method ◽  
Organization Failure

Service products are mostly produced and consumed simultaneously through interaction between customer and service providers. To prevent external failures in service operations, it is important to identify potential risks and take relevant actions to eliminate or reduce the occurrence. Therefore, risk assessment is vital to customer satisfaction in any service organization. Failure mode and effects analysis (FMEA) is an effective and useful tool for risk assessment. Although FMEA has been extensively studied in the manufacturing literature, there are a limited number of studies considering the application of FMEA in the hospitality industry. In traditional FMEA, the risk priority of failure modes is determined by generating a crisp risk priority number (RPN). However, it has been claimed in the literature that crisp RPN doesn't have a good performance in reflecting real-life situations. To overcome this shortcoming, a fuzzy hybrid FMEA method is developed. The proposed method has been tested on a case study in a five-star hotel to assess its applicability and benefits.

scholarly journals Identifying preventive maintenance guideline for a combine harvester with application of failure mode and effect analysis technique

2020 ◽  
Vol 319 ◽  
pp. 01004
Author(s):  
Voraya Wattanajitsiri ◽  
Rapee Kanchana ◽  
Surat Triwanapong ◽  
Kittipong Kimapong
Keyword(s):  
Risk Assessment ◽  
Failure Mode ◽  
Preventive Maintenance ◽  
Risk Priority Number ◽  
Effect Analysis ◽  
Analysis Technique ◽  
Combine Harvester ◽  
A Chain ◽  
Near Future

The objective of this research was to study a risk assessment of the rice combine harvester using FMEA technique implementation and suggested the procedures to maintain the parts of the rice combine harvester by analyzing the causes of risk assessment of FMEA. The FMEA was also applied to specify failure causes and effects that occurred in the rice harvester. The obtained data were calculated for a risk priority number (RPN) and then sorted to be a descending order. The high RPN part was analyzed for the causes and effects and then suggested a preventive maintenance in near future. The results revealed that the highest RPN of 576 was found when a chain surface was considered and also showed the maximum risk among the considered parts in the rice combine harvester. While, the lowest RPN of 144 was found when a rice sieve part was considered but this RPN was still higher than that of 100 RPN which was required to specify the preventive maintenance.

Failure Mode and Effect Analysis (FMEA) for Enhancing Reliability of Water Tube Boiler in Thermal Power Plant

2016 ◽  
Vol 8 (02) ◽  
pp. 79-86
Author(s):  
Kapil Dev Sharma ◽  
Shobhit Srivastava
Keyword(s):  
Power Plant ◽  
Failure Mode ◽  
Thermal Power Plant ◽  
Failure Modes ◽  
Thermal Power ◽  
Effect Analysis ◽  
Water Tube ◽  
Potential Failure ◽  
Fmea Method ◽  
Continuous Power

Failure mode and effect analysis is one of the QS-9000 quality system requirement supplements, with a wide applicability in all industrial fields. FMEA is the inductive failure analysis instruments which can be defined as a methodical group of activities intended to recognize and evaluate the potential failure modes of a product/ process and its effects with an aim to identify actions which could eliminate or reduce the chance of the potential failure before the problem occur. The purpose of this paper is to evaluate the FMEA research and application in the Thermal Power Plant Industry. The research will highlight the application of FMEA method to water tubes (WT) in boilers with an aim to find-out all the major and primary causes of boiler failure and reduce the breakdown for continuous power generation in the plant. Failure Mode and Effect Analysis technique is applied on most critical or serious parts (components) of the plant which having highest Risk Priority Number (RPN). Comparison is made between the quantitative results of FMEA and reliability field data from real tube systems. These results are discussed to establish relationships which are useful for future water tube designs.

scholarly journals Identifikasi Potensi Modus Kegagalan Yang Dapat Menghambat Kelancaran Proses Pelayanan Rawat Jalan Menggunakan Failure Mode Effect Analysis (FMEA) Di Rumah Sakit BM Jakarta Barat

2020 ◽  
Vol 8 (2) ◽  
pp. 105-113
Author(s):  
Achmaddudin Sudiro
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Outpatient Service ◽  
Outpatient Services ◽  
Effect Analysis ◽  
Outpatient Unit ◽  
Mode Effect ◽  
Potential Failure ◽  
Fmea Method ◽  
Failure Mode Effect Analysis

Outpatient services hosted by the hospital have never been absent from public visits. In fact, every year an outpatient visitor is always increasing. This research intends to identify potential failure mode that can  inhibit of every flow of service in the outpatient care unit using the Failure Mode Effect Analysis (FMEA) method. Qualitative research plan using an observation survey approach and in-depth interviews with the outpatient service head Coordinator conducted in February 2020 on the hospital outpatient unit service process. The results of this study Indicate the potential failure mode that has the value of the RPN above the value of cut off point 180 as many as six out of ten failure modes. Firstly, the check is not on schedule (360), secondly, the patient lags a turn call order Check (270), third, Specific drug failure is not available (245), fourth, general patient protests with the price of the drug (224), fifth, the patient is void to poly (196), the sixth patient registrant online missed sequence number queue (180). Based on the results of the research, hospitals are expected to follow up with the results of this research by conducting a redesign of the process that occurs today using the FMEA to maintain service quality.

scholarly journals Analisa defect produk sheet area corrugator 301 menggunakan metode SPC dan FMEA di PT Indah Kiat

2020 ◽  
Vol 12 (3) ◽  
pp. 332
Author(s):  
Evan Mandala Putra ◽  
Sri Mukti Wirawati ◽  
Pugy Gautama
Keyword(s):  
Process Control ◽  
Production Process ◽  
Statistical Process Control ◽  
Failure Mode ◽  
Failure Modes ◽  
Uneven Surface ◽  
Statistical Process ◽  
Effect Analysis ◽  
Product Defect ◽  
Fmea Method

This study aims to analyze defects in the sheet production process in the 301 Corrugator area by analyzing the total number of sheets produced and the number of sheets that have been damaged over a certain period of time using the Statistical Process Control (SPC) method and Failure Modes and Effect Analysis (FMEA). Based on the research results, there are 6 defects, namely untidy cuts, wrinkled sheets, uneven surface, curved sheets, uneven sides, loose sheet layers. The most dominant defect is uneven surface, which is 185.141 Kg or 60%. Based on the value of the RPN table, the product defect that has the highest value is the loose sheet layer with an RPN value of 245 from the calculation stage of the RPN value, a suggestion is made to reduce defects resulting from the loose sheet layer. From the stage of making improvements, the company should prioritize and focus on the types of disabilities and types of disabilities that have the highest RPN ranking when using the Failure Mode and Effect Analysis (FMEA) method.

scholarly journals A Proactive Risk Assessment Through Healthcare Failure Mode and Effect Analysis in Pediatric Surgery Department

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Yasamin Molavi-Taleghani ◽  
Hossein Ebrahimpour ◽  
Hojjat Sheikhbardsiri
Keyword(s):  
Risk Assessment ◽  
Failure Mode ◽  
Pediatric Surgery ◽  
Failure Modes ◽  
Cross Sectional Study ◽  
Related Factors ◽  
Cross Sectional ◽  
Effect Analysis ◽  
Acceptable Risks ◽  
Surgery Department

Background: Patient safety is the first step to improve the quality of care. Objectives: Therefore, the present study aimed to examine the risk assessment of processes in a pediatric surgery department using the Health Failure Mode and Effect Analysis (HFMEA) in 2017 - 2018. Methods: In this research, a mixed-method design (qualitative action and quantitative descriptive cross-sectional study) was used to analyze failure mode and their effects. The nursing errors in the clinical management model were used to classify failure modes, and the theory of inventive problem solving was used to determine a solution for improvement. Results: According to the five procedures selected by the voting method and their rating, 25 processes, 48 sub-processes, and 218 failure modes were identified with HEMEA. Eight risk modes (3.6%) were found as non-acceptable risks and were transferred to the decision tree. The main root causes (hazard score ≥ 4) were as follows: Technical-related factors (14.34%), organizational-related factors (31.9%), human-related factors (45.3%), and other factors (7.6%). Conclusions: The HFMEA method is very effective in identifying the possible failure of treatment procedures, determining the cause of each failure mode, and proposing improvement strategies.

Fuzzy-FMSA: Evaluating Fault Monitoring and Detection Strategies Based on Failure Mode and Symptom Analysis and Fuzzy Logic

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Carlos Alberto Murad ◽  
Arthur Henrique de Andrade Melani ◽  
Miguel Angelo de Carvalho Michalski ◽  
Adherbal Caminada Netto ◽  
Gilberto Francisco Martha de Souza ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Failure Mode ◽  
Failure Modes ◽  
Monitoring Techniques ◽  
Knowledge Based ◽  
Diagnosis And Prognosis ◽  
Kaplan Turbine ◽  
Shaft System ◽  
Fmea Method ◽  
Criticality Analysis

Abstract Failure mode and symptoms analysis (FMSA) is a relatively new and still not very much employed variation of failure modes, effects and criticality analysis (FMECA), a technique broadly used in reliability, safety, and quality engineering. While FMECA is an extension of the well-known failure mode and effects analysis (FMEA) method, primarily used when a criticality analysis is required, FMSA focuses on the symptoms produced by each considered failure mode and the selection of the most appropriate detection and monitoring techniques and strategies, maximizing the confidence level in the diagnosis and prognosis. However, in the same way as FMECA and FMEA, FMSA inherits some deficiencies, presenting somewhat biased results and uncertainties intrinsic to its development, due to its own algorithm and the dependence on knowledge-based inputs from experts. Accordingly, this article presents a fuzzy logic application as a complement to FMSA in order to mitigate such uncertainties' effects. As a practical example, the method is applied to a Kaplan turbine shaft system. The monitoring priority number (MPN) obtained through FMSA is compared to the fuzzy monitoring priority number (FMPN) resulting from fuzzy logic application, demonstrating how the proposed method improves the evaluation of detection and monitoring techniques and strategies.

scholarly journals Agricultural Risk Management Using Fuzzy TOPSIS Analytical Hierarchy Process (AHP) and Failure Mode and Effects Analysis (FMEA)

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 504
Author(s):  
Peyman Zandi ◽  
Mohammad Rahmani ◽  
Mojtaba Khanian ◽  
Amir Mosavi
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Failure Mode ◽  
Analytical Hierarchy Process ◽  
Failure Modes ◽  
Fuzzy Topsis ◽  
Control Measures ◽  
Management Tool ◽  
Agricultural Risk ◽  
Hierarchy Process

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.

A Novel Entropy Measure for Interval-Valued Intuitionistic Fuzzy Set and Its Application to Failure Mode and Effect Analysis

2021 ◽  
pp. 115-134
Author(s):  
Kamal Kumar ◽  
Naveen Mani ◽  
Amit Sharma ◽  
Reeta Bhardwaj
Keyword(s):  
Failure Mode ◽  
Fuzzy Set ◽  
Failure Modes ◽  
Intuitionistic Fuzzy Set ◽  
Entropy Measure ◽  
Effect Analysis ◽  
Intuitionistic Fuzzy ◽  
Safety And Reliability ◽  
Definition Of ◽  
Interval Valued

The failure mode and effect analysis (FMEA) is widely used an effective pre-accident risk assessment tool to identify, eliminate, and assess potential failure modes in different industries for enhancing the safety and reliability of systems, process, services, and products. Therefore, this chapter presents a new approach to rank the failure modes under the interval-valued intuitionistic fuzzy set (IVIFS). For this, a novel measure to measure the fuzziness known as entropy measure is proposed. Some properties and axiom definition of the proposed entropy measure have been presented to show the validity of it. Afterwards, the proposed entropy measure is utilized to obtain the weight of risk factor and developed an approach under the IVIFS environment to determine the risk priority order of failure modes. Finally, a real-life case of FMEA has been discussed to manifest the developed approach, and obtained results are compared with the results obtained by the existing methods for showing the feasibility and validity of the proposed approach.

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