FFKD-CGhostNet: A Novel Lightweight Network for Fault Diagnosis in Edge Computing Scenarios

Abstract

In recent years, deep learning (DL)-based fault diagnosis methods have witnessed significant advancements and successful applications in engineering practice. However, the increasing complexity of network structures demands higher computational resources in terms of floating point operations per second (FLOPS) and parameters, which poses challenges when deploying diagnostic models in edge computing scenarios with limited run resources. To address this issue, this study proposes a novel lightweight network, namely a cheap ghost network (CGhostNet), incorporating fine-grained feature knowledge distillation (FFKD). FFKD-CGhostNet leverages CGhostNet, a lightweight architecture, and transfers diagnostic knowledge from ResNet, a complex yet high-performing network, through FFKD. Extensive experiments are conducted on two test benches to demonstrate that FFKD-CGhostNet achieves comparable diagnostic performance to ResNet while significantly reducing parameter count by nearly 88 times and computational requirements by almost 14 times. These findings highlight the effectiveness of FFKD-CGhostNet in achieving superior diagnostic performance in resource-constrained edge computing scenarios.