## 1 Introduction
Due to the interaction between the subsystems of fault-identification objects, the fault signals usually appear in multi-scale form. Therefore, conventional CNN methods, which only contain single-scale convolution kernels, may ignore the fault-related information. To address the challenge, several multi-scale network structures have been proposed and have achieved impressive performance. Introducing multi-scale transformation into conventional CNN can enable the network to acquire features of different receptive fields at the same level and improve the diversity and complementarity of fault-related features. For example, [PERSON] et al. [18] proposed a multi-scale convolutional neural network structure that can effectively extract multi-scale high-level features. The method was verified on a WT gearbox. [PERSON] et al. [19] proposed a multiscale kernel CNN to capture the patterns of motor faults. [PERSON] et al. [20] used a similar multi-branch structure in the feature learning process. Despite the good performance, those multi-scale CNNs mentioned above simply combine the captured multi-scale features without taking into account differences in the importance of different branches. Therefore, the information related to the fault may not be used effectively, especially in the case of noise interference and load variation. In this paper, the fault-diagnosis object EMA often needs to operate under complex working conditions, such as the complexity and variability of work tasks, the non-linearity and non-stationarity of the signal caused by changes in speed and load, as well as strong environmental noise. To reduce the influence of the above factors, it is required that the intelligent diagnosis algorithm has better adaptability and can reduce the sensitivity to various uncertainty sources. In recent years, attention mechanisms have been widely utilized to achieve efficient resource allocation and information-capture capability of models for intelligent fault diagnosis [21], natural language processing, machine vision, and the broader fields of deep learning [22; 23]. [PERSON] et al. [24] demonstrated the effectiveness of attention mechanisms in bearing intelligent fault diagnosis by locating input data segments and visualizing network-learned diagnosis knowledge. [PERSON] et al. [25] proposed a wind turbine blade intelligent anomaly-detection method based on attention mechanism, which solves the memory occupancy problem of the input sequence and improves the accuracy of anomaly detection. [PERSON] et al. [26] proposed an attentional recursive autoencoder hybrid model classification algorithm for the early fault diagnosis of rotating machinery. The network is able to extract the most valuable features from the input signal. In order to make the network fully capture the fault-related features at different scales and levels and reduce sensitivity to various sources of uncertainty, a multi-scale feature fusion CNN (MSFFCNN) is herein proposed to diagnose EMA faults. The multi-scale network structure is applied to EMA fault diagnosis for the first time. Moreover, unlike the aforementioned multi-scale methods, an attention mechanism module is used. Firstly, the attention module enables effective recalibration of feature channels, enhancing essential features and suppressing invalid ones. This improves the model's ability to focus on the most relevant information. Secondly, it further recalibrates features learned by each branch and multiscale fusion features, aiming to aggregate the optimal multiscale features. This results in improved feature representation and better model performance. To verify the performance of the proposed algorithm, an EMA system experimental platform was built, and the fault injection experiment was carried out for several common faults. The collected data were used to train and test the proposed method compared with the state-of-the-art algorithms, especially under variable load and noise conditions. This paper is organized as follows.