Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties

• Proposes a novel performance-based FDI strategy using a first-order Takagi-Sugeno-Kang fuzzy inference system to handle measurement uncertainties in gas turbine engines. • Introduces a robust structure combining a specialized fuzzy inference system with the TSK-based FDI system, achieving 2%–8% improvement in success rate under large measurement bias conditions. • Utilizes parameter correction and power-level angle scheduling to reduce system complexity and rule count, enhancing computational efficiency. • Demonstrates superior accuracy in fault detection, isolation, and identification compared to existing methods, with desirable online performance.