Multiscale Graph-Guided Convolutional Network With Node Attention for Intelligent Health State Diagnosis of a 3-PRR Planar Parallel Manipulator

The data-driven intelligent health state diagnosis strategy has been successfully applied in modern industrial equipments. However, many of existing research works suffer from two major deficiencies: First, the sample independence assumption is widely adopted, so the influences of the relationship between samples on the overall performances are not explored. Second, most of the abovementioned application objects are typical key functional components (such as bearings, gearboxes, etc.), and research works on the intelligent health state diagnosis of planar parallel manipulator are rarely reported. To address these issues, a novel intelligent health state diagnosis method, termed multiscale graph-guided convolutional network with node attention (MSGCN-NA), is proposed for a 3-PRR (P and R represent prismatic and revolute pairs, respectively) planar parallel manipulator. Specifically, the developed MSGCN-NA model mainly contains the following two parts: first, the one is an unsupervised convolutional autoencoder, which is employed for the extraction of deep representation features, and then combined with Pearson metric to establish the adjacency matrix. Second, The other part is the constructed multiscale graph convolutional network, in which the node attention mechanism is adopted to achieve cross-scale fusion of different neighborhood information. The effectiveness of the proposed MSGCN-NA method is fully verified based on the simulation and experimental scenarios, the results show that MSGCN-NA can achieve superior diagnosis performances.