## 1 Introduction  
The Unmanned Aerial Vehicle (UAV), as a typical complex electromechanical system, has been used in military and commercial fields widely, but is also accompanied with a high fault rate. Improving the competence of fault diagnosis and ground maintenance, so as to improve the functionality and reliability of UAV, which is as important as exploring the modern UAV control methods such as using a brain-computer interface [1], has currently become the subject of further research emphasis [2; 3; 4]. With the continuous development of Prognostics Health Management (PHM) technology, a great quantity of sensors are employed in the new generation of large fixed-wing UAV, bringing the explosive growth of flight data scale [5]. The data-driven methods, thanks to the growth of the data scale, are gradually replacing the traditional Physics of Failure (PoF) methods [6], becoming the mainstream for fault diagnosis. Most of the common faults of current UAVs could be detected and alarmed in real time by the advanced Built-In-Test (BIT) system [7], but single faults or over-limit states of one component are reflected poorly. In actual test flight activity of UAVs, there are multiple BIT alarms showing at the same time frequently, to the extent that no such \"normal\" state would appear during a test flight procedure, at the extreme. These BIT records with concurrent alarms are laborious for the ground team to directly use in fault diagnosis and maintenance efficiently in the test flight phase, thus it is worthy to explore utilizing BIT records properly. In recent years, with a rapid development of artificial intelligence technology, fault diagnosis based on machine learning models has achieved considerable progress. The fault diagnosis process could be abstracted as a classification task in machine learning, including supervised learning classification and unsupervised learning classification. This article mainly discusses the fault diagnosis method based on supervised learning classification. The traditional machine learning methods, Decision Tree [8], Support Vector Machine [9], k-Nearest Neighbor [10], Artificial Neural Network [11], etc., have been successfully applied in the field of fault diagnosis. These models with simple structure also have the following deficiencies: annoying data preprocessing, insufficient ability to deal with high-dimensional and imbalanced data, maladaptation in multi-class classification and noticeable overfitting. To a certain extent, applying advanced attractive ensemble learning and deep learning methods in fault diagnosis might avoid these deficiencies. On the premise of the identical meta classifier, the ensemble learning algorithm could improve the performance of classification through bagging or boosting. A Random Forests fault diagnosis model was proposed in rolling bearings [12]. By negotiation and voting mechanism, the results of multiple multi-layer perceptrons were integrated to construct an ensemble learning model for bearing faults diagnosis of an induction motor [13]. To boost the performance of the models trained by small-dataset thyroid nodule ultra-sound images, a multi-view ensemble learning method was given based on voting mechanism [14]. More suitable for processing large-scale data, the deep learning algorithm based on a neural network adopts a deep hierarchy structure to represent abstract features and learns useful features in high-dimensional input automatically, with the powerful ability of multi-layer nonlinear feature learning. Including three stacking AE, a rolling bearing and gear diagnosis, tasks were realized by automatically separating useless health information [15]. A bearing fault diagnosis method with RNN in the form of an autoencoder was proposed through the multiple vibration value of the rolling bearings in [16]. To diagnose faults in the aircraft fuel system, a combination of classical Deep Belief Network (DBN) and Quantum-inspired Neural Network was proposed in [17].