Abstract
At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.
Highlights
With the continuous development of unmanned aerial vehicles (UAV), their function and structure are becoming more and more complex
Experiments and order verifythe the effectiveness effectiveness ofofthe mode selection method In In order totoverify thetroubleshooting troubleshooting mode selection method based on SIF-Support vector machine (SVM) that has been proposed in this paper, we used the fault record text data based on SIF-SVM that has been proposed in this paper, we used the fault record text data accumulated by a new fixed-wing UAV
UAV troubleshooting mode selection this paper, we proposed a text-driven
Summary
With the continuous development of unmanned aerial vehicles (UAV), their function and structure are becoming more and more complex. In order to reduce the cost, UAVs usually have low redundancy designs, so the failure rate of UAVs is higher than that of manned aircraft. When a UAV has a fault or generates an early warning, the on-site maintenance personnel usually locate the faulty system according to the warning, and decide on which maintenance method to adopt. In this process, a large number of tests need to be carried out, and professional personnel and equipment are needed. Using accumulated historical data to develop a preliminary troubleshooting method for locating system faults indicated by the UAV early warning system would improve maintenance efficiency, reduce costs, and improve failure rates
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