# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
### Contributions  
Facing these challenges, model compression techniques, such as parameter quantization, pruning, and knowledge distillation, have emerged as crucial research areas. These techniques aim to reduce the storage size and computational complexity of deep learning models while maintaining their performance. Among these, parameter quantization, which reduces the precision of model weights and activations from 32-bit floating-point to lower-bit representations (e.g., 8-bit integers), has proven effective in decreasing memory usage and computational costs. This enables the deployment of deep learning models on edge devices with limited resources, facilitating real-time fault diagnosis in industrial settings. However, the application of these techniques in bearing fault diagnosis remains underexplored, particularly in scenarios where operating conditions such as load, speed, and environmental noise vary significantly.  
Moreover, the issue of domain shift further complicates the deployment of fault diagnosis models. In real-world industrial scenarios, the distribution of data collected under different working conditions can vary significantly, leading to degraded model performance when applied to new domains. Traditional fault diagnosis models trained on data from one set of operating conditions often fail to generalize to new conditions, limiting their practical applicability. Domain adaptation techniques, which aim to align the feature distributions of source and target domains, have shown potential in addressing this challenge. By enabling models to learn domain-invariant features, these techniques can improve the robustness and generalization capabilities of fault diagnosis systems across diverse operating conditions. Motivated by these, a novel lightweight diagnosis framework is proposed for the resource-constrained embedded systems. The main contributions are presented as follows:  
* The proposed method introduces a novel combination of the S-transform and MobileNet for bearing fault diagnosis. While the S-transform provides a high-resolution time-frequency representation of vibration signals, capturing both the temporal and spectral characteristics of faults, MobileNet serves as an efficient backbone network for feature extraction. This integration leverages the strengths of signal processing and deep learning, enabling the model to extract discriminative features from complex vibration data. Unlike traditional methods that rely on handcrafted features or less efficient deep learning architectures, this approach ensures robust fault detection while maintaining computational efficiency, making it suitable for real-time applications.
* A significant innovation in this work is the application of knowledge distillation to compress the MobileNet backbone, addressing the challenge of deploying deep learning models on edge devices with limited computational resources. By training a lightweight student model to mimic the behavior of a larger teacher model, the proposed method achieves a balance between accuracy and efficiency. This approach not only reduces the model's size and computational complexity but also retains the rich feature representations learned by the teacher model. This innovation is particularly impaction for industrial applications, where real-time fault diagnosis on embedded systems is critical.
* The proposed technical route establishes an end-to-end framework that seamlessly integrates vibration signal acquisition, time-frequency analysis, deep feature extraction, and model compression. Unlike traditional fault diagnosis methods that often require separate stages for feature extraction and classification, this framework unifies the entire process into a cohesive system. The use of MobileNet and knowledge distillation ensures that the framework is not only accurate but also computationally efficient, enabling real-time deployment. This holistic approach represents a significant advancement in bearing fault diagnosis, offering a practical solution for industrial monitoring and maintenance.