scholarly journals 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 ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 313 ◽  
Author(s):  
Lelin Lv ◽  
Huimin Li ◽  
Lunyan Wang ◽  
Qing Xia ◽  
Li Ji
Keyword(s):  
Failure Mode ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Water Diversion ◽  
Weighted Averaging ◽  
Effect Analysis ◽  
Intuitionistic Fuzzy ◽  
Interval Valued ◽  
Multimoora Method ◽  
Evaluation Information

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.

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.

Dynamic prioritization of equipment and critical failure modes

Kybernetes ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 1913-1941 ◽  
Author(s):  
Mohamadreza Mahmoudi ◽  
Hannan Amoozad Mahdiraji ◽  
Ahmad Jafarnejad ◽  
Hossein Safari
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Fuzzy Topsis ◽  
Aggregation Operator ◽  
Power Weight ◽  
Equipment Failure ◽  
Content Type ◽  
Risk Prioritization ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

Purpose The purpose of this paper is to identify critical equipment by dynamically ranking them in interval-valued intuitionistic fuzzy (IVIF) circumstances. Accordingly, the main drawbacks of the conventional failure mode and effects analysis (FMEA) are eliminated. To this end, the authors have presented the interval-valued intuitionistic fuzzy condition-based dynamic weighing method (IVIF-CBDW). Design/methodology/approach To realize the objective, the authors used the IVIF power weight Heronian aggregation operator to integrate the data extracted from the experts’ opinions. Moreover, the multi-attributive border approximation area comparison (MABAC) method is applied to rank the choices and the IVIF-CBDW method to create dynamic weights appropriate to the conditions of each equipment/failure mode. The authors proposed a robust FMEA model where the main drawbacks of the conventional risk prioritization number were eliminated. Findings To prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results. Originality/value In this paper, the authors have improved and used the IVIF power weight Heronian aggregation operator to integrate information. Furthermore, to dynamically weigh each equipment (failure mode), they presented the IVIF-CBDW method to determine the weight of each equipment (failure mode) based on its equipment conditions in the O, S and D criteria and provide the basis for the calculation. IVIF-CBDW method is presented in this study for the first time. Moreover, the MABAC method has been performed, to rank the equipment and failure mode. To analyze the information, the authors encoded the model presented in the robust MATLAB software and used it in a real sample of the HL5000 crane barge. Finally, to evaluate the reliability of the model presented in the risk ranking and its rationality, this model was compared with the conventional FMEA, fuzzy TOPSIS method, the method of Liu and the modified method of Liu.

scholarly journals A Novel Hybrid Approach for Risk Evaluation of Vehicle Failure Modes

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 661
Author(s):  
Wencai Zhou ◽  
Zhaowen Qiu ◽  
Shun Tian ◽  
Yongtao Liu ◽  
Lang Wei ◽  
...  
Keyword(s):  
Failure Mode ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Hybrid Approach ◽  
Maintenance Cost ◽  
Hybrid Scheme ◽  
Analytic Hierarchy ◽  
Effect Analysis ◽  
Maintenance Time ◽  
Order Preference

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes.

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