# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
###### Abstract
Recently, cost-sensitive learning methods have become a popular means to solve the imbalance classification problem by considering the different misclassification costs of different classes [16; 17]. In fault diagnosis, [PERSON] et al. proposed a cost-sensitive active learning bidirectional gated recurrent unit (CSALBGRU) to reduce the effect of class imbalance [18]. An error cost function model was designed to guide the Convolutional Neural Network (CNN) parameters optimization in the direction of feature classification and was applied to the heavy-duty industrial robot system diagnosis procedure [19]. By using ensemble learning, [PERSON] et al. proposed a Dynamic Updated Ensemble (DUE) for learning imbalanced data streams with concept drift [20]. These methods evade the cost setting problem due to the difficulty of obtaining the objective and accurate misclassification cost, which means that the imbalanced problem is only related to the difference in data distribution. The effect of cost-sensitive methods still depends on the setting of the cost weight, which is also a difficulty in complex mechanical systems with unknown data distribution [15]. In fault diagnosis, the fault effect difference caused by characteristics of different fault modes cannot be ignored. Setting the misdiagnosis cost according to FMECA (Failure Mode, Effects and Criticality Analysis) [21] can eliminate the subjective effect of traditional expert scoring methods [22], and fix the scalability deficiency caused by training data deviation. MetaCost framework [23], applicable to arbitrary cost matrices [24], is an ensemble wrapping technique that generates cost-sensitive classifiers by relabeling original training data based on minimizing loss function. [PERSON] et al. found that using MetaCost would achieve the lowest classification cost by comparing multiple classification approaches [25]. With its enhanced robust and sensitivity, [PERSON] and [PERSON] proposed a MetaCost-based combined method to process medical data [26]. Based on preferable meta-classifiers and ensemble patterns, the MetaCost method can improve the diagnosis performance in accuracy, total misdiagnosis cost, and computational resource occupation, in response to the actual demand of UAV fault diagnosis. For identifying fault modes with more limited computational resources, this paper discusses a two-step ensemble cost-sensitive diagnosis method based on a static cost matrix. The contributions of this paper are as follows: (1) according to the fault criticality from FMECA information, we defined the misdiagnosis hazard value and calculated the misdiagnosis cost. Using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis result. (2) A two-step ensemble cost-sensitive method based on the MetaCost Framework was proposed using the stratified bootstrapping, choosing the Light Gradient Boosting Machine (LightGBM) as a meta-classifier, and adjusting the ensemble form, to enhance the overall performance of the diagnosis model and reduce the strain on computing resources, while optimizing the total misdiagnosis cost. The rest of this article is structured as follows: Section 2 provides a detailed description of the operation and maintenance data of the UAV discussed in this paper, the cost matrix setting method based on FMECA information, and a two-step ensemble cost-sensitive method based on the MetaCost framework. The application of and experiments with the proposed cost matrix setting method and the two-step ensemble cost-sensitive diagnosis method are described in Section 3. Section 4 presents a comparison of the methods applied to a KPG component of UAV, and the discussion. Finally, the conclusions are given in Section 5.