A Cloud-edge Collaborative Architecture for Data-driven Health Condition Monitoring of Machines

With the development of the Industrial Internet of Things (IIoT), various industrial intelligent applications are emerging, especially Prognostics and Health Management (PHM). The Health Condition Monitoring(HCM) of machines is an important part of PHM and has an essential purpose to improve the intelligence level of industrial machines. However, traditional monitoring methods are no longer suitable for the current adaptability, latency, bandwidth, and privacy requirements in IIoT. In this paper, we propose a novel data-driven HCM architecture. Firstly, the Knowledge Distillation (KD) and a threshold method are respectively proposed for the cloud-edge collaborative training and inference mechanism. Secondly, a health condition representation method of multisensor signal data is proposed. Finally, experiments on the public rolling element bearing dataset show that our method can significantly improve latency and save bandwidth while ensuring accuracy.