Dynamic Reconstruction Feature-Based Graph Attention Networks for Semi-Supervised Process Fault Diagnosis

Author:

Yan Peng,Liu Qiang

Publisher

Elsevier BV

Reference22 articles.

1. A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems;Alauddin;Industrial & Engineering Chemistry Research,2018

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3. Brody, S., Alon, U., and Yahav, E. (2021). How attentive are graph attention networks? arXiv preprint arXiv:2105.14491.

4. Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

5. Deep residual learning for image recognition;He;In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016

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