A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

作者：Lu, Feng; Wang, Yafan^*; Huang, Jinquan; Wang, Qihang

来源：International Journal of Turbo and Jet Engines, 2016, 33(3): 253-264.

DOI：10.1515/tjj-2015-0029

• turbofan engine
• performance diagnosis
• sensor fault diagnosis
• extended Kalman filter
• adaptive genetic algorithm

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

• 出版日期2016-9
• 单位南京航空航天大学