YAO Wen-rong1, XU Tian-zhen2, ZHANG Hai-bo2.Fault Diagnosis of Gas Path Sonsor for Turbofan Engine[J].航空发动机,2017,43(5):54-61
Fault Diagnosis of Gas Path Sonsor for Turbofan Engine
DOI:
Key Words:online sequential extreme learning  turbofan engine  sensor  fault isolation  fault diagnosis  signal reconstruction
Author NameAffiliationE-mail
YAO Wen-rong1, XU Tian-zhen2, ZHANG Hai-bo2 1 China Aerospace Power Control System Research Institute, Wuxi Jiangsu 214063, China
2. Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China 
272231671@qq.com 
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Abstract:In order to diagnose malfunctioning turbofan engines' sensor, a corresponding fault diagnosis system was designed with the Online Sequential Extreme Learning (OS-ELM) algorithm. The core idea is that after finding some malfunction sensor, a predictive learning mechanismis applied to construct fault detection and isolation for the sensor. The fault diagnosis for multiple-sensor failures can be effectively solved by this mechanism. Meanwhile, the output layer weight vector of the algorithm net is updated selectively based on generalization capability, the method could significantly improve the really-time of fault diagnosis system. Simulations on a turbofan engine show that the diagnosis method of sensor faults could detect and isolate faults of single-sensor and double-sensor failures, which also prove the validity and feasibility of the algorithm.
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