MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios
Bo Zhao, Qiqiang Wu, Ke Zhao, Zhenling Mo, Zijun Zhang, Xianmin Zhang
Abstract
As a classical and crucial component in industrial systems, the manipulators are widely employed in precision manufacturing scenarios because of their advantages of high stiffness, large load support capability, and high precision. During their service, it is inevitable that they encounter data imbalance scenarios due to the occasional and low-frequency failure behaviors. But in order to address these issues, the majority of the approaches already in use need the assistance of extra tools. Thus, a novel intelligent health state diagnosis model, named multiple neighbor homogeneous property-embedded graph auto-encoder (MNHP-GAE), is developed to get around this restriction and apply it to the manipulators. Its core is to realize the expansion and enrichment of the feature space by mining effective complementary information from homogeneous property samples without the assistance of data augmentation and other technologies. Specifically, the wavelet decomposition reconstruction and dynamic time warping are integrated to promote the quantification of the sample similarity and enable the construction of homogeneous property graph samples. Following that, a unique graph auto-encoder module with the multi-head attention mechanism is constructed to extract complementary information from homogeneous property nodes and match it for diagnostic tasks. Finally, through a multi-case experimental validation scenario constructed by a 3-PRR planar parallel manipulator experimental platform, the superior performances of the proposed MNHP-GAE model in highly unbalanced scenarios are fully demonstrated.