Fault Diagnosis of Wheeled Robot Based on Prior Knowledge and Spatial-Temporal Difference Graph Convolutional Network

Abstract

The critical issue of wheeled robot fault diagnosis is to comprehensively evaluate its health condition using multisensor data, but traditional deep learning-based methods are hard to model the relationships among sensor measurements. Unlike these methods, the graph convolutional network (GCN), which uses the graph-structured data along with the association graph as input, is more efficient for relationship modeling. However, existing GCN-based fault diagnosis methods suffer from the following weaknesses: the association graphs are obtained according to the similarity of data samples or their features, which cannot guarantee accuracy; and these models are focused on spatial correlations and neglect temporal correlations. To address these problems, we propose to construct the association graph based on prior knowledge, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., a simplified mathematical model of the wheeled robot. Moreover, we develop a spatial-temporal difference graph convolutional network (STDGCN) for wheeled robot fault diagnosis. This network contains a difference layer that utilizes localized difference properties for feature enhancement, and the spatial-temporal graph convolutional modules are introduced to jointly capture the spatial-temporal correlations. To verify the effectiveness of the STDGCN for fault diagnosis, experiments are carried out, and the results show that the STDGCN achieves superior performance.