## 5 Conclusions  
In the process of UAV flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by a BIT system. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally  
Figure 10: Confusion matrix of modified CNN.  
employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. In response to this requirement, this paper proposed a compound fault labeling and diagnosis method based on actual flight data and BIT records of a UAV during the flight test phase. With reference to FMECA and ground maintenance experience, we labeled the flight data with compound fault modes corresponding to concurrent single faults and over-limit states, and presented a novel fault diagnosis package based on XGBoost, LightGBM and modified CNN, upgraded from the original ensemble learning and deep learning models. The experimental results based on three instances of test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance.  
The following conclusions were drawn:  
1. [label=(0)]
2. Validated by manual sampling inspection, the proposed automatic labeling method is accurate and effective on actual UAV test flight data;
3. When processing imbalanced large-scale flight data, compared with the original ensemble learning and neural network method, the performance of the proposed methods has a comprehensive improvement, which has better support for the following ground maintenance.
4. It could be concluded that the superfast LightGBM-based model would play an essential role when facing the demand of real-time model updating and online diagnosis in the on-board early warning system.  
This work would be practical in the actual UAV test flight process, and also profitable for the fault diagnosis of other large complex equipment (such as high-speed trains and manned aircrafts). It is feasible to focus on the real-time updating and online diagnosis of the model based on the data stream in the future. For the faults that may cause critical damage, the cost-sensitive learning method can be introduced in future, to reduce the total cost of misdiagnosis. Through thermal imaging or other methods to analyze the damage of the UAV structure, it could further improve the comprehensive fault diagnosis and prediction of UAV, to promote the capability of health management in the future.  
Conceptualization, K.Z.; Data curation, K.Z.; Formal analysis, K.Z. and J.W.; Funding acquisition, G.J.; Investigation, K.Z. and L.Y.; Methodology, K.Z. and L.Y.; Project administration, G.J.; Resources, G.J.; Software, K.Z. and J.W.; Supervision, G.J.; Validation, K.Z. and L.Y.; Visualization, L.Y.; Writing--original draft, K.Z.; Writing--review and editing, K.Z. All authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc., via emails from our system or assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technical Research Foundation of China (Grant No. JSZL2016601A004) and National Natural Science Foundation of China (Grant No. 71772010).  
Not applicable.  
Restrictions apply to the availability of these data. The data in this research came from BHUAS Technology Co., Ltd. Please contact [PERSON] ([EMAIL_ADDRESS]) for information about the data availability.  
The work is sponsored by the Technical Research Foundation of China (Grant No. JSZL2016601A004) and Natural Science Foundation of China (Grant No. 71772010).  
The authors declare no conflict of interest.