Affiliation:
1. School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Abstract
This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Graduate Student Education and Development Foundation of Beihang University
Reference47 articles.
1. Yang, L., Jia, G., and Wei, F. (2021). The CIPCA-BPNN failure prediction method based on interval data compression and dimension reduction. Appl. Sci., 11.
2. Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations;Turan;Turk. J. Eng.,2021
3. Controlability of multi-rotors under motor fault effect;Asadi;Artıbilim: Adana Alparslan Türkeş Bilim Ve Teknol. Üniversitesi Fen Bilim. Derg.,2021
4. Huang, C., Xu, Q., and Wang, Y. (2022). Self-Supervised Masking for Unsupervised Anomaly Detection and Localization. IEEE Trans. Multimed.
5. Gordill, J.D., Celeita, D.F., and Ramos, G. (2022, January 1–5). A novel fault location method for distribution systems using phase-angle jumps based on neural networks. Proceedings of the 2022 IEEE/IAS 58th Industrial and Commercial Power Systems Technical Conference (I&CPS), Las Vegas, NV, USA.