Canonical triangular interval type-2 fuzzy set linguistic distribution assessment TODIM approach: A case study of FMEA for electric vehicles DC charging piles

Tian, Yongting; Song, Shouxu; Zhou, Dan; Pang, Shaocong; Chen, Wei

doi:10.1016/j.eswa.2023.119826

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…(2023) ventured into construction projects, integrating machine learning, fuzzy logic and FMEA for requirement prioritization. In the electric vehicle charging sector, Tian et al . (2023) introduced fuzzy set linguistic distribution combined with FMEA.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Hassan et al (2023) ventured into construction projects, integrating machine learning, fuzzy logic and IJQRM 41,4 FMEA for requirement prioritization. In the electric vehicle charging sector, Tian et al (2023) introduced fuzzy set linguistic distribution combined with FMEA. This method illuminated risk factors with subtlety, enabling informed decisions in this rapidly evolving domain.…”

Section: Integrating Fuzzy Logic and Fmeamentioning

confidence: 99%

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Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Resende,

Dedini,

Eckert

et al. 2023

IJQRM

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PurposeThis study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.Design/methodology/approachBased on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.FindingsThe results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.Practical implicationsThis study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.Originality/valueThis paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Integrating Fuzzy Logic and Fmeamentioning

confidence: 99%

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Resende,

Dedini,

Eckert

et al. 2023

IJQRM

View full text Add to dashboard Cite

show abstract

“…The theory of multi-fuzzy N-soft information emerged from work conducted by Fatimah and Alcantud [4] that combined the multi-fuzzy set theory with the N-soft set theory. Furthermore, a non-iterative reasoning technique under the consideration of fuzzy cognitive function was invented by Al Farsi et al [5], Karimi et al [6] examined the perceptual computer for hierarchical portfolio consideration in the presence of type-2 fuzzy information, and Tian et al [7] evaluated the canonical triangular interval type-2 fuzzy linguistic distribution assessment.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Applications of Artificial Intelligence in Digital Education Based on Complex Fuzzy Clustering Algorithms

2023

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Digital education is very important and valuable because it is a subpart of artificial intelligence, which is used in many real-life problems. Digital education is the modern utilization of digital techniques and tools during online purchasing, teaching, research, and learning and is often referred to as technology-enhanced learning or e-learning programs. Further, similarity measures (SM) and complex fuzzy (CF) logic are two different ideas that play a very valuable and dominant role in the environment of fuzzy decision theory. In this manuscript, we concentrate on utilizing different types of dice SM (D-SM) and generalized dice SM (GD-SM) in the environment of a CF set (CFS), called CF dice SM (CFD-SM), CF weighted dice SM (CFWD-SM), CF generalized dice SM (CFGD-SM), and CF weighted generalized dice SM (CFWGD-SM), and also derived associated outcomes. Furthermore, to evaluate or state the supremacy and effectiveness of the derived measures, we aim to evaluate the application of artificial intelligence in digital education under the consideration of derived measures for CF information and try to verify them with the help of several examples. Finally, with the help of examples, we illustrate the comparison between the presented and existing measures to show the supremacy and feasibility of the derived measures.

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Fuzzy FMEA in Risk Assessment of Human-Factor in Production Process

Łapczyńska

2023

Lecture Notes in Mechanical Engineering

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Canonical triangular interval type-2 fuzzy set linguistic distribution assessment TODIM approach: A case study of FMEA for electric vehicles DC charging piles

Cited by 7 publications

References 59 publications

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Proposal of a facilitating methodology for fuzzy FMEA implementation with application in process risk analysis in the aeronautical sector

Analysis and Applications of Artificial Intelligence in Digital Education Based on Complex Fuzzy Clustering Algorithms

Fuzzy FMEA in Risk Assessment of Human-Factor in Production Process

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