Fault diagnosis of mobile robot based on dual-graph convolutional network with prior fault knowledge

Effective integration of multi-sensor measurements is crucial for mobile robot fault diagnosis. However, in multi-sensor relationship modeling, existing methods often neglect the impact of different fault types and fail to consider the relations among data samples. To address these issues, a novel dual-graph convolutional network with prior fault knowledge (FKDGCN) is proposed. Specifically, we construct multi-sensor topological graphs based on prior fault knowledge, which effectively consider the impact of fault categories on sensor correlations. Subsequently, sample affinity graphs are constructed based on the temporal relationship and data similarity, and a sample correlation feature extraction module (SCFEM) is designed to capture the interdependence among data samples. Eventually, a novel dual-graph convolutional network is proposed to fuse multi-sample features and multi-sensor spatial–temporal features, in which more comprehensive fault information can be extracted. The effectiveness of FKDGCN is thoroughly validated on datasets collected from a real robot fault diagnosis test bench. Experimental results indicate that FKDGCN achieves outstanding diagnosis performance compared to state-of-the-art methods, with an average accuracy of over 98% on the balanced dataset and over 90% on two imbalanced datasets.