Three-Types-of-Graph-Relational Guided Domain Adaptation Approach for Fault Diagnosis of Nuclear Power Circulating Water Pump
Wei Cheng, Le Zhang, Ji Xing, Xuefeng Chen, Zelin Nie, Shuo Zhang, Song Wang, Rongyong Zhang, Qian Huang
Abstract
Existing domain adaptation methods strive to align all domains equally under a single domain shift dimension, which poses two problems. On the one hand, multiaspect domain transferring factors and homogenous alignment may lead to suboptimal results in more distant domains. On the other hand, such a global alignment ignores local discriminatory information, making class boundary samples susceptible to misclassification. Hence, the three-types-of-graph-relational guided domain adaptation (TGGDA) is proposed. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain graph</i> is formed based on condition-dependent slow variables. The domain discriminator is redesigned to reconstruct the domain graph. Second, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intrinsic</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">penalty graphs</i> are integrated to draw the same class but different domains sample closer and vice versa. The TGGDA is a system-assisted cross-domain diagnosis method that enables multidimensional domain information measurable, and the adjacency alignment allows for more accurate diagnostic results. Finally, experiments on gearbox fault diagnosis in circulating water pumps show that TGGDA can improve diagnosis accuracy.