Abstract
The traditional fault diagnosis method of aircraft navigation system monitors the system integrity through redundant information, which has extremely high requirements on the accuracy of the model, and the gradual fault detection time is long, which cannot meet the real-time nature of aircraft navigation. In this paper, the minimum cost function corresponding to the threshold is constructed by using the probability of false alarms and the probability of missed failures in the system, and the threshold of the system is obtained by the minimum value of the cost function. In addition, the actual navigation data is preprocessed, and the corresponding labels are sent to the convolutional neural network for training to obtain a fault diagnosis model. Using One-Class SVM to train normal navigation data, the fault diagnosis of the navigation system is realized. Using the above three methods, a comprehensive discriminant system is constructed to diagnose the faults of the navigation system. Experiments show that the detection time of the comprehensive discrimination mechanism for slowly changing faults is significantly shorter than that of the threshold method, and the fault diagnosis accuracy rate reaches more than 97%.
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Mao, Y., Deng, S., Chen, D., Qin, N., Huang, D. (2023). Fault Diagnosis of Aircraft Navigation System Based on Machine Learning. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_144
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DOI: https://doi.org/10.1007/978-981-19-6613-2_144
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