# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
## 1 Introduction  
Rolling bearings are widely used in rotating machinery because of their simple structure, small friction coefficient, high motion accuracy, sensitive start, and low manufacturing cost [1]. In actual working conditions, rolling bearings are often subject to alternating loads, excessive loads, poor lubrication, improper assembly, electrical discharge, etc. It is easy for wear, pitting, plastic deformation, corrosion, cracks, and other forms of defects to form, affecting the normal operation of the equipment, and even causing significant economic losses and injuries [2]. According to a survey conducted by the IEEE Industry Application Society (IEEE-IAS) and the Japan Electrical Manufacturers Association (JEMA), bearing failures are the most common type of mechanical equipment component failures, accounting for about 30-40% of all failures [3]. Therefore, to ensure the safety and reliability of mechanical equipment, especially the stable operation of bearings, it is particularly important to establish an efficient intelligent health detection and fault diagnosis system. The data-driven fault diagnosis method based on vibration signal analysis has been emerging in the field of rolling bearings, injecting new elements into intelligent bearing fault diagnosis. The traditional intelligent diagnosis method includes two key steps: data feature extraction and fault classification [4]. Time domain, frequency domain, and time-frequency domain methods are used for feature extraction, while shallow models such as support vector machines, artificial neural networks, and k-nearest neighbors are usually used for fault classification [5]. However, these methods have some problems, such as an over-reliance on complex signal processing and diagnostic experience, and the strong subjectivity and poor generality of artificial feature extraction. Moreover, researchers also found that the diagnostic performance of the shallow model was greatly reduced when faced with more complex nonlinear problems. These problems significantly affect the actual effect and applicability of traditional intelligent diagnosis methods. In recent years, deep learning has played a key role in making up for the shortcomings of traditional methods in rolling bearing fault diagnosis and has promoted new researchprogress. After deep Convolutional Neural Networks (CNNs) made remarkable achievements in the field of image processing, researchers introduced them into the field of fault diagnosis and have carried out in-depth research, creating a new possibility for improving the accuracy and efficiency of fault diagnosis. By applying CNNs, researchers were able to more effectively capture key features in rolling bearing fault data, further improving the performance level of diagnostic systems. The successful application of this method not only supports the application of deep learning in theory but also shows remarkable results in practical rolling bearing fault diagnosis. [PERSON] et. al. proposed an intelligent method of fault diagnosis, which they referred to as the Efficient Convolutional Transformer (ECTN), which produces better diagnostic performance in variable operating conditions [6]. [PERSON] et. al. proposed an intelligent fault diagnosis of rolling bearings based on an improved AlexNet [7]. [PERSON] et. al. proposed an improved fault diagnosis method for one-dimensional (1D) and two-dimensional (2D) convolutional neural networks [8]. [PERSON] et. al. proposed a simplified global information fusion convolution neural network (SGIF-CNN) [9]. Through an analysis of the literature, it can be seen that deep neural networks have achieved remarkable results in fault diagnosis, mainly by building complex networks, gradually increasing the depth, and introducing a variety of processing methods to improve performance.