Abstract
Aiming at the problem of low precision of actuator fault identification under closed loop control of aircraft, a fault diagnosis algorithm was proposed, which extracted fault feature by ensemble empirical mode decomposition (EEMD) and principal component analysis (PCA), classified by AdaBoost and adaptive support vector machine (AdaBoost-ASVM). Firstly, in terms of feature extraction, the fault signal was decomposed into intrinsic mode functions (IMFs) of different frequency range by EEMD to avoid the modal mixing phenomenon in empirical mode decomposition (EMD). Secondly, the features of IMF were calculated from multiple dimensions. Thirdly, those high-dimensional features were analyzed by PCA to extract the main features that contain fault information. AdaBoost-ASVM algorithm was proposed to solve the problem of low accuracy of traditional multi-class SVM. Multiple SVM based classifiers with weak learning ability were constructed into a strong classifier through ensemble learning. Adaptive parameter optimization was carried out for each SVM based classifier. Simulation results shows that, compared with traditional multi-class SVM algorithm and some other machine learning algorithms, proposed method improves the accuracy of fault diagnosis and provides an effective and feasible method for the identification of aircraft actuator fault state under closed-loop control system.
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Acknowledgements
The research is supported by National Natural Science Foundation of China, NO. 61673209, 71971115, 71471087, Graduate Innovation Base Open Foundation of NUAA, NO. kfjj20190320.
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Wei, R., Jiang, J., Xu, H., Sun, X., Chen, Y. (2022). Fault Diagnosis of Aircraft Actuators Based on AdaBoost-ASVM. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_12
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DOI: https://doi.org/10.1007/978-981-15-8155-7_12
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