Fault Diagnosis for Mobile Robots Based on Spatial–Temporal Graph Attention Network Under Imbalanced Data
Longda Zhang, Zhaoming Miao, Yingxiang Xia, Fengyu Zhou, Xianfeng Yuan
Abstract
The key of mobile robot fault diagnosis is to model the spatial-temporal correlations from multi-sensor data. However, the majority of existing deep learning (DL)-based studies only focus on extracting either temporal or spatial information. Additionally, data imbalance problem, which seriously affects models’ generalization performance, cannot be ignored in robot fault diagnosis. To address these issues, a novel spatial-temporal graph attention network (STGATN) is proposed, which has three primary characteristics: 1) the feature-enhanced spatial-temporal graph is constructed based on robot multi-sensors data; 2) an attention-based spatial-temporal feature extraction module (A-STFEM) is designed to mine spatial-temporal correlation information among multi-sensors; 3) a regulatory cross-entropy loss function is developed to enhance the model robustness under imbalanced data scenario. The effectiveness of STGATN is verified by fault diagnosis experiments for a wheeled mobile robot and results show that STGATN can achieve outstanding diagnosis performance and robustness.