Blockchain-of-Things-Based Edge Learning Contracts for Federated Predictive Maintenance Toward Resilient Manufacturing
Jiewu Leng, J F Guo, Dewen Wang, Yuanwei Zhong, Kailin Xu, Sihan Huang, Jiajun Liu, Chunyang Yu, Zhipeng Ye, Qiang Liu
Abstract
Social manufacturing leverages the power of social networks and collaborative processes to enhance manufacturing capabilities and supports the sharing of ideas, resources, and information. However, traditional remote maintenance under a social manufacturing context lacks resilience against disruptions and cyber attacks. These issues often lead to interruptions in production. This article proposed blockchain-of-things-based edge learning contracts for federated predictive maintenance (FPM). First, given the diversity and heterogeneity of equipment, an open platform communication unified architecture (OPCUA)-based equipment meta-model is proposed to facilitate the interconnection and data sharing. Second, a Blockchain-of-Things-based secure access control approach is proposed, to directly collect data from controllers. This approach prevents tampering, unlike traditional local database collection methods. Third, to address the security and efficiency needs, an edge learning contract method is proposed for FPM. An integrated learning algorithm based on smart contracts is designed to achieve prediction performance that is comparable to local centralized training while reducing data transmission load and enhancing data security. Finally, a federated predictive maintenance platform is designed and implemented to enhance the system’s resilience, and its effectiveness is verified through case studies.