# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
###### Abstract  
This paper presents a lightweight fault diagnosis framework for bearing defects, integrating time-frequency analysis, deep learning, and model compression techniques to address challenges in resource-constrained environments. The proposed method combines the S-transform for high-resolution time-frequency representation with MobileNet as an efficient backbone network, enabling robust feature extraction from complex vibration signals. To enhance deployment on edge devices, knowledge distillation is employed to compress the model, significantly reducing computational complexity while maintaining diagnostic accuracy. Additionally, domain adaptation is considered to mitigate domain shift issues, ensuring robust performance across varying operating conditions. Experimental results demonstrate the framework's effectiveness, achieving high diagnostic accuracy with reduced computational overhead, making it a practical solution for real-time industrial applications. The proposed approach bridges the gap between advanced deep learning techniques and practical industrial requirements, offering a scalable and efficient solution for bearing fault diagnosis. fault diagnosis; lightweight system; model compression; domain adaptation +
Footnote †: journal: Applied sciences  
0000-0001-5002-4899-7880]X. [PERSON]  
0000-0001-5002-5880]Y. [PERSON]  
0000-0001-5002-5880]X. [PERSON]  
0000-0002-0002-3300]X. [PERSON]  
0000-0002-0002-3300]Z. [PERSON]  
0000-0002-3300-3000]X. [PERSON]  
0000-0002-3300-3300-3000]Z. [PERSON]  
0000-0002-3300-3000]X. [PERSON]  
0000-0002-3300-3300-3300-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-33000-330000-330000-330000-330000-330000-33000-330000-33000-3300 plications, particularly in the context of fault diagnosis [1]. Traditional fault diagnosis systems often rely on cloud-based solutions, where data are transmitted to remote servers for analysis [2]. However, this approach introduces latency, bandwidth limitations, and privacy concerns, making it unsuitable for time-sensitive applications, such as bearing fault diagnosis [3]. Edge computing addresses these challenges by bringing computation and data storage closer to the data source, enabling real-time analysis and decision-making at the edge of the network. In the context of bearing fault diagnosis, edge computing allows for the deployment of lightweight diagnostic models directly on industrial machinery, facilitating immediate detection and response to faults. Recent studies have explored the integration of edge computing with machine learning techniques for fault diagnosis [4]. For instance, [PERSON] et al. proposed a lightweight reprogramming framework for cross-device fault diagnosis, which includes cloud-based C-model training and edge-based E-model reprogramming and application stages [5]. Similarly, [PERSON] et al. proposed a fault location scheme for distribution grids by utilizing edge computing, which is aimed at ensuring data privacy and security [6]. These works highlight the potential of edge computing to revolutionize fault diagnosis by enabling real-time, on-device analysis while reducing reliance on centralized cloud infrastructure. There are also related studies that have proposed a data augmentation method combining continuous wavelet transform and a deep convolutional generative adversarial network (DCGAN), as well as a hybrid data-driven scheme combined with physics-based sample generation (PSG), which have made contributions to the lightweighting of the fault diagnosis model [7,8]. **Model Compression**. Model compression has become a crucial research area in deep learning, particularly for applications requiring deployment on resource-constrained devices such as edge nodes. The main goal of model compression is to reduce the storage size and computational complexity of the neural networks while maintaining the calculation performance. The model compression techniques include pruning [9], quantization, and knowledge distillation.