Fuzzy reliability analysis of a robotic system using single-valued neutrosophic sets based on score, accuracy and certainty functions

Abstract

In modern engineering systems, uncertainties and incomplete information often
pose significant challenges in accurately evaluating system reliability. Traditional probabilistic
and fuzzy approaches are insufficient to effectively capture indeterminacy and inconsistency
inherent in real-world environments. To address these limitations, this study proposes a neu
trosophic framework for fuzzy reliability analysis of a robotic system using single-valued neu
trosophic sets (SVNS). In the proposed approach, the reliability of each system component is
represented by a neutrosophic triplet characterized by truth, indeterminacy, and falsity mem
bership degrees. A fuzzy success fault tree model is developed to analyze the reliability struc
ture of the robotic system, incorporating both series and parallel configurations of components
such as motors, sensors, rollers, and bearings. The overall system reliability is computed using
neutrosophic aggregation operators. Furthermore, to ensure a comprehensive and consistent
evaluation, three decision-making measures namely score, accuracy, and certainty functions are
employed. These functions establish a total order on neutrosophic numbers, enabling complete
and unambiguous assessment of system reliability. A numerical example of a robotic system
is presented to demonstrate the applicability and effectiveness of the proposed methodology.
The results indicate that the neutrosophic-based approach provides a more flexible, robust,
and realistic representation of uncertainty compared to conventional fuzzy reliability models.
The inclusion of the certainty function enhances the decision-making process by ensuring com
pleteness in ranking and improving the reliability evaluation framework.

DOI 10.5281/zenodo.20348924