## 1 Introduction  
Due to the rapid development of semiconductor integrated circuits and digital control technology as well as the many problems existing in traditional airborne actuation systems, more electric aircraft (MEA) technology has become the main trend of modern aviation development. It emphasizes the usage of electric power instead of hydraulic power, pneumatic power, etc. ; consequently, it has the advantages of light weight, high power density and efficiency, low cost, simple testing and maintenance, etc. [1]. At present, the most representative MEA of civil aircraft are Airbus A380 and Boeing 787 aircraft, and that of military aircraft is the F-35 fighter. Airbus A380 and F-35 fighter have already used electro-hydrostatic actuators to drive the primary flight control surface. Boeing 787 aircraft, on the other hand, applies electromechanical actuators (EMAs) to the secondary flight controls, like salt actuators and spoiler actuators [2]. With the advancement of power-by-wire technology, EMA, which completely abandons the hydraulic system, is bound to have better application prospects. EMA can be divided into two forms according to different driving methods: ball screw EMA with gear reduction mechanism and direct-drive ball screw EMA. The direct-drive EMA, which directly integrates the ball screw pair with the motor in its structure, eliminates the need for a gear reduction mechanism, so it has the advantages of high reliability, high efficiency, and high integration. However, as an emerging technology, achieving the same level of reliability as hydraulic servo systems is challenging for direct-drive EMA, thus limiting its large-scale application [3]. In order to ensure the safe operation and economic maintenance of aircraft, it is of great significance to accurately and timely diagnose and predict the real-time status of direct-drive EMA. In recent years, data-driven intelligent fault diagnosis are widely used in the field of fault diagnosis. In most of the existing data-based EMA fault-diagnosis methods, feature extraction and classification are designed and perform separately rather than as a single entirety, so both cannot be optimized simultaneously. For example, dynamic response indicators derived from vibration and current signals of EMA can be utilized as classification features [4; 5]. Mode decomposition [6] and information entropy [7] are also used to constitute the feature vector of EMA. Additionally, principal component analysis (PCA) is often used for the selection and fusion of EMA fault features [8; 9]. After selecting the appropriate features, these features are fed into a classifier, such as BP neural network, support vector machine, and random forest, for fault classification [10; 11]. The features of the aforementioned traditional data-based methods usually rely on manual design, requiring certain signal processing technology and diagnostic expertise, which can be unreliable and time-consuming. Moreover, they are mostly used in specific fields and cannot be updated online with changes in application equipment or fields. To address the defects of traditional fault-diagnosis approaches, deep learning methods such as stacked denoising autoencoder, deep belief network, and convolutional neural network (CNN) [12; 13] have demonstrated great vitality in these years. Unlike traditional methods, deep learning can directly learn the effective fault features adaptively from monitoring signals and perform the fault classification at the same time, thereby achieving end-to-end fault diagnosis. Particularly, the CNN has achieved superior performance in various fault-diagnosis tasks [14; 15; 16] due to its features of weight sharing, local connection, and multiple convolution kernels. The superior performance of one-dimensional CNN (1 DCNN) in the EMA fault-diagnosis problem compared to traditional data-based methods has been demonstrated in prior work [17]. Therefore, this paper aims to develop a CNN-based intelligent fault-diagnosis model for EMA.