TY - GEN
T1 - A Cloud-edge Collaborative Architecture for Data-driven Health Condition Monitoring of Machines
AU - Shi, Jianxin
AU - Wang, Rui
AU - Xian, Muqing
AU - Wo, Tianyu
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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 multi-sensor 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.
AB - 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 multi-sensor 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.
KW - Cloud-edge collaboration
KW - Health Condition Monitoring
KW - Industrial Internet of Things
KW - Knowledge Distillation
UR - https://www.scopus.com/pages/publications/85125910749
U2 - 10.1109/JCC53141.2021.00020
DO - 10.1109/JCC53141.2021.00020
M3 - 会议稿件
AN - SCOPUS:85125910749
T3 - Proceedings - 2021 IEEE International Conference on Joint Cloud Computing, JCC 2021 and 2021 9th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2021
SP - 51
EP - 58
BT - Proceedings - 2021 IEEE International Conference on Joint Cloud Computing, JCC 2021 and 2021 9th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Joint Cloud Computing, JCC 2021 and 2021 9th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2021
Y2 - 23 August 2021 through 26 August 2021
ER -