Fuzzy failure modes and effects analysis by using fuzzy Vikor and Data Envelopment Analysis‐based fuzzy AHP

Baloch, Aman Ullah; Mohammadian, Hossin

doi:10.21833/ijaas.2016.08.005

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…In recent times, only few researchers have integrated DEA model with fuzzy to enhance decision making of taking adequate corrective actions in FMEA. Some previous attempts include the efficiency evaluation of risk factors in Baloch, integration of quantitative risk assessment (QRA) and FMEA in Kengpol and Tuammee, and using DEA with respect to different weights of risk factors as input and undesirable cost as output . It is good to put on record that in the coming time, DEA is very likely to be used more often in evaluating FMEA.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.

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Section: Introductionmentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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“…Various types of information describing the heterogeneous features of risk factors were applied and clustered in [30], and a DEA model was utilized to rank failure modes using the values of risk factors as inputs. Additionally, in [31], fuzzy AHP, fuzzy VIKOR, and DEA (without input or output) models were integrated to determine the weights of each risk factor, where the relation of maximum to minimum weight was considered. Failure modes were obtained through the SHELL (software, hardware, environment, and liveware) model analysis in [32], where TOPSIS was used to determine the expert weights.…”

Section: Fmea and Dea Methodsmentioning

confidence: 99%

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

Chnina,

Daneshvar

2024

Applied Sciences

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The failure mode causes and effects analysis (FMCEA) is a commonly used reliability approach. It identifies, predicts, and analyzes potential failure modes affecting the proper function of equipment or the process under study, along with their roots and consequences. FMCEA aims to evaluate and assess the risks resulting from their occurrence, intending to suggest corresponding repair, adjustment, and precautionary measures to be planned during the conception, instruction, or implementation stages. However, the FMCEA has been criticized in the literature for its many inherent shortcomings related to risk assessment and prioritization. Therefore, this study presents an enhanced FMCEA method to address the deficiencies of the traditional risk priority number (RPN) and improve the reliability of risk assessments and corrective actions. A data envelopment analysis (DEA), as a non-parametric method, is used to evaluate the efficiency of these failures by considering their fixing time and cost and deciding on their final priority ranks. Sub-failure modes and their interrelationships are also taken into account. The radio frequency identification (RFID) system was chosen as an example due to its core role in Industry 4.0 and the Internet of Things (IoT) to demonstrate the effectiveness and usefulness of the proposed method. A total of 67 failures related to both hardware and software parts, including the environmental impacts of this technology, have been disclosed. The results of the conventional and the suggested FMCEA methods are found to be considerably different, with ten failure modes classified as being the most efficient.

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“…Mandal et al [50] presented a methodology utilizing VIKOR approach for ranking the human errors. Baloch et al [51] integrated fuzzy VIKOR method and data envelopment analysis method into FMECA for determining the rankings of potential manners and selecting the most important impairment manner.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System

Wang

Deng

et al. 2022

Energies

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Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.

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Fuzzy failure modes and effects analysis by using fuzzy Vikor and Data Envelopment Analysis‐based fuzzy AHP

Cited by 10 publications

References 15 publications

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

An Integrated Approach-Based FMECA for Risk Assessment: Application to Offshore Wind Turbine Pitch System

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