Fuzzy Logic-Based Robustness Assessment of Complex Engineering Systems: Methodologies, Case Studies, and Comparative Analysis

Abstract

Robustness assessment of engineering systems under uncertainty is a critical factor for ensuring operational reliability and minimizing costly downtime. Traditional probabilistic methods often fall short in addressing the vagueness and ambiguity inherent in real-world data and expert judgments. This study presents an in-depth application of fuzzy logic approaches—including Fuzzy Fault Tree Analysis (FFTA), Fuzzy Reliability Block Diagrams (FRBD), and Fuzzy Bayesian Networks (FBN)—to model and evaluate the robustness of complex engineering systems. Detailed case studies on an automated manufacturing plant and a power distribution system demonstrate how fuzzy models effectively capture uncertainty through membership functions and fuzzy arithmetic, providing a richer reliability assessment than classical methods. Sensitivity analyses identify the most influential parameters affecting system robustness, guiding targeted maintenance and risk mitigation. A comparative evaluation of the fuzzy methods highlights their respective strengths and limitations, informing model selection based on application requirements. The results underscore fuzzy logic’s potential to enhance fault diagnosis, maintenance prioritization, and decision-making under uncertainty. Finally, future research directions emphasize real-time fuzzy monitoring and hybrid AI integration to meet the demands of increasingly complex and interconnected systems.