# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 1 Introduction  
Rolling bearings, which are crucial components of mechanical equipment, are prone to various types of damage due to their long running time and harsh working environment [1]. If damaged components cannot be detected and replaced promptly, outcomes include disruption of the normal operation of the mechanical equipment, lower production efficiency, and even serious consequences such as substantial economic losses and casualties [2]. Accurate, real-time detection and diagnosis of the operating condition of rolling bearings is therefore essential [3]. In fault diagnosis (FD), vibration signals are widely used as effective diagnostic signals due to their ability to quickly and accurately reflect equipment operating conditions [4, 5, 6]. Traditional FD methods mainly rely on signal-decomposition techniques such as feature mode decomposition [7], all time-scale decomposition [8] and Blaschke mode decomposition [9]. These methods identify fault types by analyzing the characteristics of the vibrationsignal, but they heavily rely on empirical knowledge and are difficult to interpret for people without relevant expertise [10]. Compared to signal-decomposition methods, deep learning techniques have been more widely researched and applied in industry because they do not require a priori knowledge and have superior feature-extraction capability [11; 12]. For instance, [PERSON] et al. [13] proposed an FD model combining a one-dimensional convolutional neural network (CNN) with a two-dimensional improved ResNet-50, which utilizes multi-modal information to enhance the robustness of the model. [PERSON] et al. [14] proposed the MT-ConvFormer FD model, which utilizes multi-task learning to extract fault information. [PERSON] et al. [15] proposed an FD model based on multimodal data fusion and the subtraction-average-based optimizer to address the challenge of poor model generalization with small samples by combining CNN and support vector machines. Although these approaches effectively improve FD results by introducing various mechanisms, they also require extensive parameter calculations, leading to increased consumption of storage and computational resources. In recent years, the rapid development of embedded components and digital twin technology have provided new solutions for FD in rolling bearings in special operating scenarios [16]. Due to the advantages of edge computing, such as fast transmission and low latency, the lightweight FD methods for rolling bearings have attracted significant attention in recent research [17]. To enable real-time diagnosis, [PERSON] et al. [18] proposed a lightweight, multi-scale, multi-attention feature fusion network that significantly enhances fault feature identification. [PERSON] et al. [19] proposed an AUTO-CNN architectural design method, aiming to achieve a balance between accuracy and use of computational resources. [PERSON] et al. [20] designed a lightweight CNN and fine-tuned it using the energy-valley optimizer. [PERSON] et al. [21] proposed a lightweight FD model that integrates frequency-slice wavelet transform with an improved neural-transformer that emphasizes the global capture of important fine-grained information. [PERSON] et al. [22] designed a lightweight pyramid attention residual network for fault diagnosis under changing operating conditions. However, while the above methods have the advantages of their light weights and the promising results they achieve through different technical approaches, they are over-reliant on global information and neglect to utilize the information extracted at different stages for a more comprehensive diagnosis. Although lightweight models offer advantages such as low computational cost and high diagnostic speed, they face several challenges in practical applications. For instance, issues such as limited fault data and noise interference in real-world scenarios significantly hinder the practical deployment of lightweight models [23; 24; 25; 26].