## 2 Structure and Faults Analysis of the Direct-Drive EMA
### Fault Categories and Data Processing  
From the perspective of the composition of EMA, there are four types of failures that may occur during operation: motor failure, electrical failure, mechanical failure, and sensor failure. Due to the harshness of the actual application conditions of EMA, such as overload, harsh environment, lubrication problems, and manufacturing defects, it is prone to mechanical failures. Furthermore, as a key component of the drive servo actuation system, the motor usually runs at a higher speed, accompanied by temperature rise in the housing and obvious mechanical stress, so the motor is prone to a winding short circuit and rotor-shaft eccentricity. Electrical faults mainly refer to faults in EMA's power supply and controller and sensor faults. This paper comprehensively considers the three factors of EMA fault-occurrence frequency, degree of influence, and similarity of fault performance. The four faults of motor winding turn-to-turn short circuit, ball screw wear and jam, IGBT open circuit, and sensor deviation were selected for research. The specific experimental data and status labels are shown in Table 1. Among them, after the faults have been determined, the IGBT open circuit and the winding turns short circuit can be specific to the fault of a certain phase or a few phases according to the value of each phase's current sensor. Figure 2: Experimental setup. Taking the IGBT fault as an example, the open-circuit fault of IGBT is simulated by setting the driving signal of the IGBT to low at a certain time. Figure 3 shows the current and position signals of EMA before and after the fault. It can be seen that the fault-phase current of IGBT fault increases rapidly, and the position response is slightly deviated from that before the fault, which affects the performance of the EMA system. Under different fault conditions of EMA, the four-phase current signal output of EMA is collected at the sampling frequency of 10 kHz, and then, the sum of the four-phase current constitutes a signal. Figure 4 shows an example of the combined signals in five states, all of which have been normalized. In order to facilitate the training of the convolutional neural network and reduce the interference of different working conditions on the model, each segment of the signal x is normalized. The size of the data is an important factor of the success of deep learning. Generally speaking, the more training samples of a network model, the better its generalization performance. Therefore, in the process of deep learning model training, data augmentation technology is often used; that is, without a substantial increase in data, limited data can generate value equivalent to more data. Taking into account that the output of the electromechanical actuator is a one-dimensional sequence signal, this paper adopts the overlapping sampling method to achieve the purpose of data amplification. The sampling method is shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Status** & **Length** & **Sample Type** & **Sample Size** \\ \hline C1 & Normal & & & \\ C2 & Winding turn-to-turn short circuit & & Training & 800 \\ C3 & IGBT open circuit & 4096 & Validation & 200 \\ C4 & Ball screw wear and jam & & Test & 24 \\ C5 & Sensor deviation & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Composition of experimental samples. Figure 3: Experiment waveform of IGBT fault: (**a**) current waveform of IGBT open circuit; (**b**) position response of IGBT open circuit. The above combined signals are segmented with a certain overlap ratio so that the training set, verification set, and test set are obtained. The specific methods are as follows:  
\[\left\{ \begin{aligned} m=\left\lfloor\frac{L-l}{l\times\lambda} \right\rfloor\\ x_{i}^{\prime}=x^{\prime}[((i-1)\times l\times(1-\lambda)):((i-1) \times l\times(1-\lambda)+l)]\end{aligned} \right.