# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 2 Materials and Methods  
### Operation and Maintenance Data of UAV  
The new generation of UAVs carries numerous sensors for monitoring the operation status from self-check before takeoff to landing and shutdown. The operation data we discuss in this article is the flight data of UAVs, which is a collection of time series data points, including sensor signals and command inputs.  
The characteristics of the flight data are as follows [27]:  
* Large-scale. A single flight may record tens of thousands of data instances, which can be limited by a lack of computing memory.
* High-dimensional. Attained from the numerous sensors, the actual flight data of UAVs have hundreds of attributes, which can lead to the \"Curse of Dimensionality\".
* Multi-class. UAVs have many types of complicated fault modes, requiring the effective multi-class classification technology.
* Imbalanced. The quantity of flight data in different classes has inevitable disparities in terms of the actual operation.
* Noisy. The mission condition of UAV is so complicated that serious noise interference exists, such as the interruption of transmission or electromagnetic interference.  
Other characteristics of UAV flight data include their time-sequential nature, the fact that they are recorded at fixed intervals, their inconsistent value range and accuracy, and discontinuity (outliers and frame loss). These characteristics were very important for research focusing on signal feature extraction (e.g., bearing vibration data analysis) but did not have a significant impact on our method selection.  
The maintenance data we discuss in the present paper includes BIT (Build-in Test) records and FMECA information of UAVs. The new generation BIT system of UAVs, based on an expert system, PoF model, and fuzzy logic, etc., could automatically monitor most of the sensor signals or signal combinations that can cause a fault, by alerting one to or even interrupting operation instructions once the signals exceed the present limit. [PERSON] utilized BIT records to label the flight data of UAVs, automatically [27]. Failure modes, effects, and criticality analysis (FMECA), a widely used methodology among the safety, reliability, and risk engineers, provide valuable information for the improvement of the safety and reliability of the process and system [28; 29]. Quantitative criticality analysis (CA) could obtain the criticality value \(R_{i}\) (to prevent confusion with the cost of misclassification) of fault mode \(F_{i}\) based on the probability of fault and consequences, calculated using the following equation:  
\[R_{i}=\alpha_{i}\cdot\beta_{i}\cdot\lambda_{i}\cdot t \tag{1}\]  
where \(\alpha_{i}\), \(\beta_{i}\), \(\lambda_{i}\), and \(t\) represents the fault mode ratio taken from historical database; the conditional probability of fault effect leading to identified severity classification; the basic fault rate prediction based on a specific model; and the mission phase duration, respectively [30]. As an inductive process with a \"bottom-up\" approach, Criticality Analysis (CA), which is meant to provide information for risk management decisions, is performed in the design and operation stages of UAV. Using this information to modify the fault diagnosis model based on flight data can effectively fuse the design knowledge and operation data together.