Time graph sub-domain adaption adversarial for fault diagnosis
Time graph sub-domain adaption adversarial for fault diagnosis is a scholarly work, published in 2024 in ''Measurement Science and Technology''. The main subjects of the publication include data mining, graph, ENCODE, artificial intelligence, Extractor, convolution, computer science, fault, artificial neural network, software engineering, domain, theoretical computer science, diagnosis, adversarial system, pattern recognition, and algorithm. The experimental results show that the average diagnosis accuracy of TGSDAA can improve 4% than other methods..