A Lightweight Reprogramming Framework for Cross-Device Fault Diagnosis in Edge Computing

Abstract

Fault diagnosis in mechanical equipment is essential for industrial system stability and safety. However, when applying the model to different models of devices, the difference in sample feature distribution seriously affects the diagnosis effect. At the same time, traditional cloud-based deployment faces delays and resource constraints, making it unable to meet real-time requirements. This article introduces a lightweight reprogramming framework for cross-device fault diagnosis in edge computing environments. It mainly includes cloud-based C-model training and edge-based E-model reprogramming and application stages. The model introduces a lightweight feature extraction (LFE) module and a decoupled fully connected (DFC) attention mechanism to enhance feature representation and global information capture. Through lightweight reprogramming, the E-model fits the device data in actual engineering while maintaining the diagnostic capability of the C-model. We used the NVIDIA Jetson Xavier NX kit as an edge computing platform and conducted verification experiments. The results show that the proposed method achieves good diagnostic effects on engineering equipment. At the same time, it achieves excellent lightweight indicators.