Federated Physics-Informed Graph Framework Guided by Multianchors for Heterogeneous Wheeled Robots Collaborative Fault Diagnosis

Wheeled robot fault diagnosis is indispensable for ensuring its reliable and safe operations. However, two challenges impede the application of prevalent intelligent diagnosis methods. 1) Multisensor fusion: The complexity of robot movements necessitates multisensor for comprehensive monitoring, generating strong-coupled, and high-dimensional data that complicate both intrinsic relationship mining and effective fusion; 2) Heterogeneous data silos: Dispersibility, heterogeneity and privacy constraints across different robots lead to non-independent and identically distributed (Non-IID) data silos, severely limiting the development of universal diagnostic models. To overcome these two problems, this article proposes a tailored federated physics-informed graph framework (FedMA-PIG). On the client side, the kinematics mathematical model is constructed for each robot, which explores the inter-sensor correlations and forms a physics-informed graph. It enables multisensor data fusion and assists the client in training a local graph neural network. On the federated framework side, a Non-IID federated framework based on a multianchor contrastive mechanism is devised. It employs multiple anchors to capture common knowledge from heterogeneous robot data, guiding feature representations toward corresponding anchors and away from others, thereby promoting consistency and mitigating inter-client data heterogeneity. Comprehensive experiments were conducted on three representative wheeled robots- Mecanum-wheeled, 4WD-wheeled, and Omni-wheeled- distributed across four federated clients. The results demonstrate that FedMA-PIG achieves generalized and superior diagnostic performance compared to state-of-the-art methods.