Fetching the paper…
Reading the bibliography…
Graph neural network (GNN)-based fault diagnosis (FD) has received increasing attention in recent years, due to the fact that data coming from several application domains can be advantageously represented as graphs.
J. C. David and J. MacKay, “A practical bayesian framework fro backpropagation networks,” Neural Computation , vol. 4, pp. 448–472, 1992
1992
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
S. Ding, Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools . Berlin, Germany: Springer, 2008
2008
Earlier work this paper cites.
W. Z. A. J. Bruna and Y. L, “Spectral networks and locally connected networks on graphs,” Computing Research Repository , pp. 1–14, 2013
2013
Earlier work this paper cites.
Y. G. Lei, J. Lin, M. J. Zuo, and Z. J. He, “Condition monitoring and fault diagnosis of planetary gearboxes: A review,” Measurement , vol. 48, pp. 292–305, 2014
2014
Earlier work this paper cites.
S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Transactions on Industrial Electronics , vol. 61, no. 11, pp. 6418–6428, 2014
2014
Earlier work this paper cites.
J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, “Prognostics and health management design for rotary machinery systems-reviews, methodology and applications,” Mechanical Systems and Signal Processing , vol. 42, no. 1, pp. 314–334, 2014
2014
Earlier work this paper cites.
H. Henao, G. Capolino, and M. Cabanas, “Trends in fault diagnosis for electrical machines: A review of diagnostic techniques,” IEEE Industrial Electronics Magazine , vol. 8, no. 2, pp. 31–42, 2014
2014
Earlier work this paper cites.
S. Ding, Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems . London, UK: Springer, 2014
2014
Earlier work this paper cites.
R. Girshick, J. Donahue, and T. Darrell, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Computer Vision and Pattern Recognition , pp. 580–587, 2014
2014
Earlier work this paper cites.
K. Zhang, H. Hao, Z. Chen, S. Ding, and K. Peng, “A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches,” Journal of Process Control , vol. 33, pp. 112–126, 2015
2015
Earlier work this paper cites.
Z. Wang and T. Oates, “Imaging time-series to improve classification and imputation,” Conference on artificial intelligence , pp. 3939–3945, 2015
2015
Earlier work this paper cites.
J. Wang, F. Yang, T. Chen, and S. L. Shah, “An overview of industrial alarm systems: Main causes for alarm overloading, research status, and open problems,” IEEE Transactions on Automation Science and Engineering , vol. 13, no. 2, pp. 1045–1061, 2016
2016
Earlier work this paper cites.
C. Alippi, N. Stavros, and R. Manuel, “Model-free fault detection and isolation in large-scale cyber-physical systems,” IEEE Transactions on Emerging Topics in Computational Intelligence , vol. 1, no. 1, pp. 61–71, 2016
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , pp. 770–778, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” eural information processing systems , vol. 30, pp. 3844–3852, 2016
2016
Earlier work this paper cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” International Conference on Machine Learning , pp. 2014–2023, 2016
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” 2016
2016
Earlier work this paper cites.
Y. Liao, Y. Wang, and Y. Liu, “Graph regularized auto-encoders for image representation,” IEEE Transactions on Image Processing , vol. 26, no. 6, pp. 2839–2852, 2016
2016
Earlier work this paper cites.
Z. Ge, “Review on data-driven modeling and monitoring for plant-wide industrial processes,” Chemometrics and Intelligent Laboratory Systems , vol. 171, pp. 16–25, 2017
2017
Earlier work this paper cites.
K. Zhang, Y. Shardt, Z. Chen, X. Yang, S. Ding, and K. Peng, “A kpi-based process monitoring and fault detection framework for large-scale processes,” ISA Transactions , vol. 68, pp. 276–286, 2017
2017
Earlier work this paper cites.
X. Qi, R. Liao, J. Jia, S. Fidler, and R. Urtasun, “3D graph neural networks for RGBD semantic segmentation,” pp. 5209–5218, 2017
2017
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, and Y. Bengio, “Graph attention networks,” International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
W. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Neural Information Processing Systems , vol. 30, pp. 1025–1035, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Yang, C. Yang, T. Peng, X. Yang, and W. Gui, “A fault-injection strategy for traction drive control systems,” IEEE Transactions on Industrial Electronics , vol. 64, no. 7, pp. 5719–5727, 2017
2017
Cited alongside, same era.
H. Chen, B. Jiang, S. X. Ding, and B. Huang, “Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–17, 2020, doi:10.1109/TITS.2020.3029946
2020
Later among the works it cites.
S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. Inman, “1d convolutional neural networks and applications: A survey,” Mechanical Systems and Signal Processing , vol. 151, p. 107398, 2021, doi:10.1016/j.ymssp.2020.107398
2020
Later among the works it cites.
H. Zhang and Y. Shen, “Template-based prediction of protein structure with deep learning,” BMC Genomics , vol. 21, 2020
2020
Later among the works it cites.
Z. Zhang, P. Cui, and W. Zhu, “Deep learning on graphs: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2020, doi: 10.1109/TKDE.2020.2981333
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
H. Luo, H. Zhao, and S. Yin, “Data-driven design of fog-computing-aided process monitoring system for large-scale industrial processes,” IEEE Transactions on Industrial Informatics , vol. 14, pp. 4631–4641, 2018
2018
Cited alongside, same era.
J. Zhou, G. Cui, and Z. Zhang, “Graph neural networks: A review of methods and applications,” 2018
2018
Cited alongside, same era.
Q. Li, Z. Han, and X. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 , S. A. McIlraith and K. Q. Weinberger, Eds., pp. 3538–3545. AAAI Press, 2018
2018
Cited alongside, same era.
S. Pan, R. Hu, G. Long, J. Jiang, and L. Yao, “Adversarially regularized graph autoencoder for graph embedding,” in Proceedings of the International Joint Conference on Artificial Intelligence , pp. 2609–2615, 2018
2018
Cited alongside, same era.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in Proceedingsof the International Joint Conference on Artificial Intelligence , pp. 3634–3640, 2018
2018
Cited alongside, same era.
D. Hoang and H. Kang, “A survey on deep learning based bearing fault diagnosis,” Neurocomputing , vol. 335, pp. 327–335, 2019
2019
Cited alongside, same era.
L. Yao, C. Mao, and Y. Luo, “Graph convolutional networks for text classification,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, pp. 7370–7377, 2019
2019
Cited alongside, same era.
I. Chami, S. Abu, and B. Perozzi, “Machine learning on graphs: A model and comprehensive taxonomy,” CoRR , 2020
2020
Later among the works it cites.
D. Bacciu, F. Errica, A. Micheli, and M. Podda, “A gentle introduction to deep learning for graphs,” Neural Networks , vol. 129, pp. 203–221, 2020
2020
Later among the works it cites.
T. Li, Z. Zhao, C. Sun, R. Yan, and X. Chen, “Multi-receptive field graph convolutional networks for machine fault diagnosis,” IEEE Transactions on Industrial Electronics , 2020, doi: 10.1109/TIE.2020.3040669
2020
Later among the works it cites.
C. Li, L. Mo, and R. Yan, “Rolling bearing fault diagnosis based on horizontal visibility graph and graph neural networks,” in International conference on Sensing, measurement, data analytics in the era of artificial intelligence , pp. 275–279, 2020
2020
Later among the works it cites.
Z. Zhang, J. Huang, and Q. Tan, “SR-HGAT: Symmetric relations based heterogeneous graph attention network,” IEEE Access , vol. 8, pp. 631–645, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Philip, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 1–21, 2020
2020
Later among the works it cites.
J. He and H. Zhao, “Fault diagnosis and location based on graph neural network in telecom networks,” in International Conference on Networking and Network Applications , pp. 304–309, 2020
2020
Later among the works it cites.
T. Li, Z. Zhao, C. Sun, R. Yan, and X. Chen, “Multi-receptive field graph convolutional networks for machine fault diagnosis,” IEEE Transactions on Industrial Electronics , 2020, doi: 10.1109/TIE.2020.3040669
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Rizzo, G. Susinni, and F. Iannuzzo, “Intrusiveness of power device condition monitoring methods: Introducing figures of merit for condition monitoring,” IEEE Industrial Electronics Magazine , 2021, doi:10.1109/MIE.2021.3066959
2021
Closest in time.
Y. Zhao, X. He, J. Zhang, H. Ji, D. Zhou, and M. G. Pecht, “Detection of intermittent faults based on an optimally weighted moving average t2 control chart with stationary observations,” Automatica , vol. 123, p. 109298, 2021
2021
Closest in time.
D. Zheng, L. Zhou, and Z. Song, “Kernel generalization of multi-rate probabilistic principal component analysis for fault detection in nonlinear process,” IEEE/CAA Journal of Automatica Sinica , vol. 8, no. 8, pp. 1465–1476, 2021
2021
Closest in time.
M. Kuppusamy, A. Hussain, P. Sanjeevikumar, J. Holm-Nielsen, and V. Kaliappan, “Deep learning for fault diagnostics in bearings, insulators, pv panels, power lines, and electric vehicle applications-the state-of-the-art approaches,” IEEE Access , vol. 9, pp. 41 246–41 260, 2021
2021
Closest in time.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 4–24, 2021
2021
Closest in time.
X. Yu, B. Tang, and K. Zhang, “Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks,” IEEE Transactions on Instrumentation and Measurement , vol. 70, pp. 1–14, 2021
2021
Closest in time.
H. Li, H. Chen, and W. Wang, “A structural deep network embedding model for predicting associations between mirna and disease based on molecular association network,” Scientific Reports , vol. 11, p. 12640, 2021
2021
Closest in time.
Z. Chen, J. Xu, T. Peng, and C. Yang, “Graph convolutional network-based method for fault diagnosis using a hybrid of measurement and prior knowledge,” IEEE Transactions on Cybernetics , pp. 1–13, 2021, doi: 10.1109/TCYB.2021.3059002
2021
Closest in time.