Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

By Min Xia, Haidong Shao, Darren Williams, Siliang Lu, Lei Shu, Clarence W. de Silva. Reliability Engineering & System Safety, 2021

336 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/b18433e81db16e1ee20092ec1111ff1a52b98a44

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In Mechanical Engineering

1. Fengshou Gu

Citations in the corpus

• Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing (2023, methodology): “For instance, Xia et al. [36] developed a DT-assisted fault diagnosis method for the triplex pump, in which a simulation model of a real triplex pump was used to generate simulated fault data; and a sparse de-noising auto-encoder model based on fine-tuned was built to implement fault diagnosis.”
• Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “…DT implementation attempts so far seem to have been accomplished with the support of a number of common enabling technologies, such as: ML [17], TL [18], distributed computing (i.e., cloud, fog, and edge computing) [19], the (Industrial) Internet of Things (I/IoT) [20], CPS [21], and VR/AR [22].”