Application of a FMEA method combining interval 2-tuple linguistic variables and grey relational analysis in preoperative medical service process

Shi, Suxia; Fei, Hongying; Xu, Xueguo

doi:10.1016/j.ifacol.2019.11.368

Cited by 21 publications

(15 citation statements)

References 9 publications

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“…The grey correlation coefficient ( γ ij , i = 1, 2, 3,… m , j = 1, 2, 3, …, n ) between y j and x ij is calculated as

where ξ denotes a discriminant coefficient within the range of 0 to 1, and generally the value of ξ is taken as 0.5 [ 60 , 62 , 63 , 64 ]. The grey relational grade ( γ i ) between Y and X i is formulated as

…”

Section: Evaluation Of Critical Dynamic Stress and Final Accumulative Plastic Strainmentioning

confidence: 99%

“…The computed values of the grey correlation coefficients γ 1 j , γ 2 j , and γ 3 j are presented in Table 2 , and the grey relational grades ( γ 1 , γ 2 , and γ 3 ) of β regarding w , σ 3 , and σ d obtained by Equation (5) are 0.809, 0.745, and 0.720, respectively. Generally, the feature sequence is considered to be strongly correlated with the observation sequences if the calculated grey relational grade exceeds 0.6, as recommended by previous studies [ 62 , 63 , 64 , 68 ]. Therefore, it is considered that β (i.e., the curve pattern) is closely related to w , σ 3 , and σ d .…”

Section: Evaluation Of Critical Dynamic Stress and Final Accumulative Plastic Strainmentioning

confidence: 99%

“…where ξ denotes a discriminant coefficient within the range of 0 to 1, and generally the value of ξ is taken as 0.5 [60,[62][63][64]. The grey relational grade (γ i ) between Y and X i is formulated as…”

Section: Evaluation Of Criticalmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

et al. 2021

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Critical dynamic stress (σcri) and accumulative plastic strain (εp) are primary indicators regarding the dynamic stability of unbound granular materials (UGMs). This study aims to seek an effective method to evaluate the dynamic stability of UGMs used in railway subgrades. First, the dynamic characteristics of an UGM used in railway subgrade bed construction were investigated by performing a series of large-scale cyclic triaxial tests, with the results showing that εp versus cycle number (N) curves can be categorized into stable, failure, and critical patterns. Grey relational analyses were then established, where the analyzed results demonstrated that the εp–N curve pattern and final accumulative plastic strain (εs) of the stable curves are strongly correlated with the moisture content (w), confining pressure (σ3), and dynamic deviator stress (σd). The analyzed grey relational grades distributed in a narrow range of 0.72 to 0.81, indicating that w, σ3, and σd have similar degrees of importance on determining the εp–N curve patterns and the values of εs of the UGM. Finally, a data processing method using a back-propagation (BP) neural network is introduced to analyze the test data, and an empirical approach is developed to evaluate the σcri (considering the effects of σ3 and w) and εs (considering the effects of σ3, w, and σd) of the UGM. The analyzed results illustrated that the developed method can effectively reflect the linear/non-linear relationships of σcri and εs with respect to σ3 and/or σd. The σcri approximately increases linearly with increasing σ3, and a simple empirical formula is proposed for the σcri. In addition, εs and its variation rate increase non-linearly with increasing σd but decrease non-linearly as σ3 increases.

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“…The grey correlation coefficient ( γ ij , i = 1, 2, 3,… m , j = 1, 2, 3, …, n ) between y j and x ij is calculated as

…”

Section: Evaluation Of Critical Dynamic Stress and Final Accumulative Plastic Strainmentioning

confidence: 99%

Section: Evaluation Of Critical Dynamic Stress and Final Accumulative Plastic Strainmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

et al. 2021

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“…In the 1960s, FMEA, which provided helpful outputs in the military and aviation industries, is one of the commonly used analytical tools and techniques to classify and remove potential or recognized failures and make risk management decisions to increase the reliability and safety of systems or processes and provide the necessary support [3]. This method, which is based on the principle of identifying and correcting possible troubles before the production process, ensures the continuousness of high-level quality products by aiming to identify the core reasons of risks and reduce their impacts [4][5][6][7].…”

Section: Failure Mode and Effects Analysismentioning

confidence: 99%

Risk Analysis Application in Aviation Sector with Intuitionistic Fuzzy TOPSIS Method

Ünlükal¹,

Yücel²

2021

ujm

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“…Uncertainty is a key issue in risk analysis and management [ 1 , 2 ]. Since it was introduced by NASA in 1960s [ 3 ], failure mode and effects analysis (FMEA), as a typical bottom-up technique to model and manage potential risks [ 4 ], has been extensively applied to various industries, such as medical domain [ 5 , 6 , 7 ], aircraft landing system [ 8 ], assisted reproduction technology [ 9 ], automotive industry [ 10 ] and so on [ 11 ]. In these piratical applications, FMEA is mainly used to ensure that potential risks have been taken into account and dealt with properly during the assessment process.…”

Section: Introductionmentioning

confidence: 99%

Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis

Zheng

Tang

2020

Entropy

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Failure mode and effects analysis (FMEA), as a commonly used risk management method, has been extensively applied to the engineering domain. A vital parameter in FMEA is the risk priority number (RPN), which is the product of occurrence (O), severity (S), and detection (D) of a failure mode. To deal with the uncertainty in the assessments given by domain experts, a novel Deng entropy weighted risk priority number (DEWRPN) for FMEA is proposed in the framework of Dempster–Shafer evidence theory (DST). DEWRPN takes into consideration the relative importance in both risk factors and FMEA experts. The uncertain degree of objective assessments coming from experts are measured by the Deng entropy. An expert’s weight is comprised of the three risk factors’ weights obtained independently from expert’s assessments. In DEWRPN, the strategy of assigning weight for each expert is flexible and compatible to the real decision-making situation. The entropy-based relative weight symbolizes the relative importance. In detail, the higher the uncertain degree of a risk factor from an expert is, the lower the weight of the corresponding risk factor will be and vice versa. We utilize Deng entropy to construct the exponential weight of each risk factor as well as an expert’s relative importance on an FMEA item in a state-of-the-art way. A case study is adopted to verify the practicability and effectiveness of the proposed model.

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Application of a FMEA method combining interval 2-tuple linguistic variables and grey relational analysis in preoperative medical service process

Cited by 21 publications

References 9 publications

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

Evaluation of Critical Dynamic Stress and Accumulative Plastic Strain of an Unbound Granular Material Based on Cyclic Triaxial Tests

Risk Analysis Application in Aviation Sector with Intuitionistic Fuzzy TOPSIS Method

Deng Entropy Weighted Risk Priority Number Model for Failure Mode and Effects Analysis

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