MKDC: A Lightweight Method for Cloud-Edge Collaborative Fault Diagnosis Model

Artificial intelligence has been widely applied in the field of mechanical fault diagnosis and has achieved remarkable results. Most edge-computing environments in industrial production use embedded systems, which have limited computing power. Although deep learning models for fault diagnosis are powerful, they consume significant computational resources, making them difficult to directly apply in cloud-edge collaborative fault diagnosis systems. To reduce the feedback time and deployment cost of fault diagnosis systems, this article conducts research from two aspects: optimizing measurement points and multidimensional distillation compression. This article proposes a lightweight method for a cloud-edge collaborative fault diagnosis model based on multidimensional knowledge distillation compression (MKDC). First, by optimizing the number of measurement points, the redundancy of test data is reduced. Then, based on the structure of the knowledge distillation compression model, the aim of creating a lightweight fault diagnosis network model is achieved by compressing the fault feature extraction units and fusion units. Finally, experiments are conducted to verify the effectiveness of the proposed compression method, demonstrating its effectiveness and advancement in model compression. The lightweight method in this study enhances the cross-platform compatibility of fault diagnosis models, making them easier to deploy on different types of devices, which is of great significance for the widespread application and popularization of fault diagnosis systems.