Dynamic prioritization of equipment and critical failure modes: An interval-valued intuitionistic fuzzy condition-based model

• Published in: Kybernetes 2019-10, Vol.48 (9), p.1913-1941
• Main Authors: Mahmoudi, Mohamadreza, Amoozad Mahdiraji, Hannan, Jafarnejad, Ahmad, Safari, Hossein
• Format: Article
• Language: English
• Subjects: Agglomeration; Crane barges; Failure analysis; Failure modes; Fuzzy sets; Mathematical analysis; Ranking; Reliability analysis; Weight
• ISSN: 0368-492X; 1758-7883
• DOI: 10.1108/K-08-2018-0417

PurposeThe 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/approachTo 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.FindingsTo 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/valueIn 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.