Collaborative Swarm Intelligent Diagnosis Method for Machine Groups with Transferability Topology Planning

doi:10.3901/JME.260101

Abstract

Abstract: Machine group collaborative service is central to networked collaborative manufacturing. Performing data-centralized intelligent diagnosis for such groups faces challenges like data barriers and individual differences. To overcome data barriers and enhance model adaptability, current approaches based on federated learning and multi-domain adaptation establish decentralized diagnosis architectures. However, they exhibit blindness in group task planning and neglect uneven individual importance when integrating local models. To address this, we propose a collaborative swarm intelligent diagnosis method with transferability topology planning. First, a transferability topology structure is established to define diagnosis knowledge flow among machine nodes. Second, the topology planning is optimized by comprehensively considering data quality, available data amount, diagnosis knowledge transferability, communication resources, and individual importance, thus determining knowledge flow relationships and individual importance distribution. Finally, an individual importance-weighted decentralized diagnosis architecture is built to collaboratively train a global diagnosis model for the machine group. Validation experiments using bearing fault data from multiple devices show that the optimized transferability topology can effectively reflect knowledge flow relationships among machine nodes. This improves the diagnosis accuracy of global model and enables its adaptability for machine group diagnosis.

Key words: machine groups, swarm intelligence, collaborative intelligent diagnosis, transferability topology planning

CLC Number:

TH17

YANG Bin, LI Yaning, LEI Yaguo, LI Xiang, CAO Junyi, WU Tonghai. Collaborative Swarm Intelligent Diagnosis Method for Machine Groups with Transferability Topology Planning[J]. Journal of Mechanical Engineering, 2026, 62(4): 1-11.

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