IFWG-TOPSIS Model for Supporting Infant Failure Assessment in an Offshore Wind Turbine System

Abstract

Introduction: System failure analysis is an essential aspect of equipment management. This analysis improves equipment reliability and availability. However, to assess infant failure under dynamic criteria, reliability engineers require special models. Method: Hence, this study uses an Intuitionistic Fuzzy Weighted Geometric (IFWG) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to develop an IFWG-TOPSIS model for infant failure assessment. We consider a case study of Offshore Wind (OFW) turbine infant failure assessment. Result: During the model evaluation, this study considered an infant failure of the turbine's main shaft, blade bearings, pitch system, jacket and monopile support structure, and gearbox. Risk factor, spare part weight, technical importance, cost, and complexity criteria were used to evaluate these components’ reliability. The results show that the blade bearings (S2) and main shaft are the most and least reliable components, respectively. To validate the model’s performance, we compared its results with Gümüş and Bali’s and standard VIKOR models results. These models selected the same components as the most and least reliable components, respectively. Thus, the proposed model is suitable for OFW turbine’s infant failure assessment. Discussion: It can be deduced that the use of the modified IFWG operator to calculate the intuitionistic fuzzy distance measure in standard TOPSIS model as the capacity to produce realistic results that can compete with existing decision-making methods. Conclusion: This study has investigated the use of techno-economic criteria for OFW turbine components’ infant failure assessment. A fuzzy-based model was used to establish the connection between the criteria and the components. We developed this model using the Intuitionistic Fuzzy Weighted Geometric (IFWG) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. Using experts’ judgments, data were obtained for the developed model evaluation and validation.