# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 6 Conclusions  
UAVs are increasingly used in many industries, so fault detection and anomaly detection for UAVs are becoming increasingly important. When using machine learning and deep learning methods for fault detection of UAVs, problems arise because the logs of UAVs are difficult to obtain, features are difficult to extract, and processes and logs of UAVs are not directly usable. In this paper, a Time Line Modeling (TLM) approach was proposed to acquire and process the failure data of UAVs. First, based on the software-in-the-loop (SITL) simulation system of UAVs, a flight mission with four trajectories is constructed. The four common faults of UAVs (GPS fault, accelerometer fault, engine fault, and Remote-Control System fault) were simulated in the same flight, and the original logs of the UAV flight were collected. Secondly, the Time Line Modeling approach is specifically divided into two stages. The first stage locates the normal and abnormal data of the UAV using a Fault Point-in-time Anchoring-based approach. The second stage expands the abnormal data of the UAV using a Time-Window Stretching-based approach. Finally, the flight logs under four flight trajectories: GPS information, IMU information, RATE information, and VIBE are used as data. The time-related features, as well as non-generic features, are removed and put into the machine learning model and convolutional neural network model for training. The experimental results proved that the UAV fault data set obtained based on TLM methods was effective. In summary, this paper implemented the collection and processing of UAV fault logs using the proposed TLM method. This approach provided a new source of fault data for classifying UAV faults. Our future research will focus on developing effective models for detecting UAV faults based on the data set obtained through the TLM method.  
[PERSON] and [PERSON] contributed to the conception of the study; [PERSON] performed the experiment and wrote the manuscript; [PERSON] and [PERSON] helped perform the experiment and provided constructive discussions. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **F1-Score** & **Accuracy** & **Recall** \\ \hline Normal & 0.7552 & 0.7325 & 0.7793 \\ GPS & 0.6271 & 0.7551 & 0.5363 \\ Acc & 0.8360 & 0.8364 & 0.8355 \\ Engine & 0.6790 & 0.5516 & 0.8829 \\ RC & 0.6510 & 0.7395 & 0.5814 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Results of IMU data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **F1-Score** & **Accuracy** & **Recall** \\ \hline Normal & 0.7506 & 0.7186 & 0.7854 \\ GPS & 0.6459 & 0.7560 & 0.5639 \\ Acc & 0.8240 & 0.8687 & 0.7837 \\ Engine & 0.9182 & 0.9207 & 0.9157 \\ RC & 0.6959 & 0.6761 & 0.7169 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Results of RATE data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **F1-Score** & **Accuracy** & **Recall** \\ \hline Normal & 0.7518 & 0.6010 & 0.8849 \\ GPS & 0.4796 & 0.6666 & 0.3746 \\ Acc & 0.0000 & 0.0000 & 0.0000 \\ Engine & 0.8919 & 0.9512 & 0.8395 \\ RC & 0.3177 & 0.5622 & 0.2214 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Results of VIBE data.  
Funding: This work was supported by the Sichuan Science and Technology Program (Grant No. 2022 YFG0322), the China Scholarship Council Program (Nos. 202001010001 and 202101010003), the Innovation Team Funds of China West Normal University (No. KCXTD2022-3), the Nanchong Federation of Social Science Associations Program (Grant No. NC22C280), and China West Normal University 2022 University-level College Student Innovation and Entrepreneurship Training Program Project (Grant No. CXCY2022285).