A real-time fault diagnosis method for hypersonic air vehicle with sensor fault based on the auto temporal convolutional network

In this paper, a fault diagnosis problem for Hypersonic Air Vehicle (HAV) with sensor fault is concerned. Existing fault diagnosis models pay less attention to the problems of high-performance real-time diagnosis and Artificial Intelligence (AI) algorithm autonomous optimization. Therefore, a smart real-time fault diagnosis algorithm is put forward to automatically build a more accurate and rapid model in a short time. Based on the Temporal Convolutional Network (TCN) with tuning parameters optimizing by Strengthen Elitist Genetic Algorithm (SEGA), the Auto Temporal Convolutional Network (AutoTCN) is first proposed. To better diagnose the time-sequence sensor fault signal, the Sequential Probability Ratio Test (SPRT) method is introduced afterwards. Additionally, the Wavelet Packet Translation (WPT) is combined with TCN to enhance the mechanism and sensitivity of the extracted fault features. Experimental results from the HAV model with the Reaction Control System (RCS) control simulated under sensor fault are obtained. It is demonstrated that, for typical sensor faults greater than 8.89%, the real-time fault diagnosis accuracy of the proposed method may exceed 96%. The diagnosis delay is less than 0.05 s. Moreover, on the computer equipped with an 8-core CPU, over 87.5% of the working time can be saved.