# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 5 Conclusions  
In order to improve data-driven fault diagnosis models within the limited computing resources, a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV is proposed. Focusing on the overall utility of UAV diagnosis and maintenance, we managed to reduce the overall misdiagnosis cost of the diagnosis model as the core target, setting the misdiagnosis cost matrix by the fault criticality of FMECA, to guide the correction of the data-driven diagnosis model. Based on our two-step ensemble cost-sensitive method (MC-LGB), the capability of the diagnosis model can be enhanced significantly to support the ground maintenance decisions.  
The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost caused by misdiagnosis, without putting forward excessive requirements for computing equipment under the condition of ensuring a certain overall diagnosis performance. More specifically, compared with the other fault diagnosis models applied to the TYW-001 UAV, it is demonstrated that the proposed MC-LGB model can significantly reduce the total misdiagnosis cost from over 1700 to 1592, with an accuracy loss of only 3%. Focusing on the computing resources occupancy, the MC-LGB model also reached the average level of training time, in spite of the two-step ensemble processing.  
In essence, our method improves the utility of UAV diagnosis and maintenance by restrainedly sacrificing the global accuracy of fault diagnosis. It would inevitably follow that more \"non-serious faults\" can be misdiagnosed, as other faults or a normal state, with this method. Once the judgment of fault influence is incorrect, it can produce the fault loss more than expected so it is necessary to emphasize the reliability of the used fault criticality values in a cost matrix setting.  
It is feasible for the proposed two-step cost-sensitive model applying to actual UAV fault diagnosis. In future work, adding the ground maintenance cost and mission delay cost, caused by the misdiagnosis to the cost matrix, would be a practicable and valuable direction to explore. Since the reliable fault criticality values can be difficult to obtain in some scenarios, using the Risk Priority Number (RPN) instead of the fault criticality can also set the misdiagnosis cost matrix using our method, which requires further research on the exact RPN calculation. For the requirements of real-time fault diagnosis on UAVs in the future, the cost-sensitive diagnosis model based on the MetaCost framework cannot yet handle the data stream. Developing the cost-sensitive flight data stream mining method can be an interesting approach for fault diagnosis and the health management of UAVs.  
Conceptualization, K.Z.; methodology, K.Z. and L.Y.; software, K.Z. and C.L.; validation, K.Z. and L.Y.; formal analysis, K.Z. and C.L.; investigation, K.Z. and L.Y.; resources, G.J. and C.L.; data curation, K.Z.; writing--original draft preparation, K.Z.; writing--review and editing, K.Z.; visualization, L.Y.; supervision, G.J.; project administration, G.J. and C.L.; funding acquisition, G.J. 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 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]) to inform about the data availability.  
All authors would like to thank the Technical Research Foundation and Natural Science Foundation of China.  
The authors declare no conflict of interest.