Exploring Cotangent Similarity Measures for Enhanced Fault Diagnosis in Steam Turbines.

In the context of multi-criteria decision-making problems within single-valued neutrosophic set environments, this study introduces a simplified version of two similarity measures and develops two aggregation operators to synthesize results based on the proposed measure. Using a turbine generator fault diagnosis problem as a case study, we demonstrate that our proposed aggregation operators effectively generate diagnoses that align with checking reports. Additionally, we conduct a comprehensive examination of two cotangent similarity measures, investigating their properties and uncovering their inherent complexity as transformations of our proposed similarity measures. As a result, we advise researchers to avoid employing these more intricate measures. Our findings provide valuable guidance for practitioners dealing with multi-criteria decision-making problems in single-valued neutrosophic set environments. [ABSTRACT FROM AUTHOR]