Abstract
AbstractRobustness against measurement uncertainties is crucial for gas turbine engine diagnosis. While current research focuses mainly on measurement noise, measurement bias remains challenging. This study proposes a novel performance-based fault detection and identification (FDI) strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system. To handle ambient condition changes, we use parameter correction to preprocess the raw measurement data, which reduces the FDI’s system complexity. Additionally, the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system. The data for designing, training, and testing the proposed FDI strategy are generated using a component-level turbofan engine model. The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression. A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases. The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies. Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection, isolation, and identification. The robust structure demonstrates a 2%–8% improvement in the success rate index under relatively large measurement bias conditions, thereby indicating excellent robustness. Accuracy against significant bias values and computation time are also evaluated, suggesting that the proposed robust structure has desirable online performance. This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
Published Version
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