## 4 Results and Discussion  
The combination modes of concurrent single fault alarms from BIT records were sorted out by us, and mapped to a variety of complicated states. After data assembling and labeling with the method proposed in Section 2.2, the total data set had 285,117 instances, including 457 parameters and one label including 98 classes. With reference to the ground maintenance experience and FMECA, we analyzed all the 98 classes of states to ultimately set 49 new compound fault classes including 284,393 cases, through merging some classes corresponding to the same fault mode and deleting a few abnormal instances caused by transmission and storage, etc. In Table 1, the count of accurate automatic labeling from manual labeling is shown, randomly sampled from 200 instances from each test flight data set. In general, it shows that our approach achieved considerable success in accurate labeling. Figure 5 shows the improvement in proposed ensemble learning methods and neural network methods on main performance metrics, respectively. For precision, recall and F1 score, compared with the original GBDT model, the proposed ensemble learning methods, XGBoost in particular, had a significant performance improvement. LightGBM and modified CNN also gained obvious advantages over the original GBDT and FCNN in all metrics. In brief, the proposed methods effectively complement the shortcomings of the original models. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **No.18100223** & **No.19012332** & **No.19012433** \\ \hline Labeling accuracy & 200/200 & 200/200 & 200/200 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The count of accurate automatic labeling from manual labeling. Figure 5: The comparison of accuracy, precision, recall, F1 score, Matthews Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC) of proposed and original methods based on ensemble learning and neural network models on the actual UAV data. The details of each metric of the diagnosis models' performance are shown in Table 2. In terms of the accuracy, precision, recall and F1 score, all the proposed methods could reach the level above 0.82 on the test set, XGBoost even reaching a marvelous level above 0.99, that greatly improves the diagnosis availability of the model. Due to the serious imbalance of flight data, it is urged to pay more attention to the MCC (Matthews Correlation Coefficient) and AUC (Area Under the ROC Curve), reflecting the possibility of the model bias toward majority classes. It can be seen that every proposed method could achieve above 0.95 on MCC, with a preferable capability to previous methods. Likewise, the result of AUC explains the friendliness of proposed models to imbalanced data, which can be visualized from ROC (Receiver Operating Characteristic) curves in Figure 6.  
According to Table 2, considering that the data in training exceeded 220,000 samples, the slowest model completed training within 12 min, which is an acceptable result in processing large-scale UAV flight data. With the improvement in fault diagnosis ability, the training time of the model is also significantly increased, except for the LightGBM model. As a lightweight tool, LightGBM demonstrated its extraordinary speed in model training, shown in Figure 7, while ensuring a decent diagnostic performance. It could be concluded that the LightGBM-based model would play an essential role in the future when facing the demand of real-time model updating and online diagnosis. Comparing the three proposed methods, the confusion matrices of XGBoost, LightGBM and modified CNN are shown in Figures 8-10, respectively. It can be intuitively found that the XGBoost and modified CNN are more reliable in the diagnosis of the test set, while LightGBM has a systematic misdiagnosis for multiple frequent fault modes.