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

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作  者:Shuai Ma Yafeng Wu Zheng Hua Linfeng Gou 

机构地区:[1]Shenyang Aeroengine Research Institute,Shenyang 110066,China [2]National Key Lab of Aerospace Power System and Plasma Technology,Shenyang Aeroengine Research Institute,Shenyang 110066,China [3]School of Power and Energy,Northwestern Polytechnical University,Xi’an 710072,China

出  处:《Chinese Journal of Mechanical Engineering》2025年第1期62-83,共22页中国机械工程学报(英文版)

摘  要:Robustness 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.

关 键 词:Performance-based fault diagnosis Gas turbine engine Fuzzy inference system Measurement uncertainty Regression and classification 

分 类 号:V263.6[航空宇航科学与技术—航空宇航制造工程] V263.2

 

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