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Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest.
K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European conference on computer vision . Springer, 2010, pp. 213–226
2010
Earlier work this paper cites.
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,” Machine learning , vol. 79, no. 1-2, pp. 151–175, 2010
2010
Earlier work this paper cites.
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE Transactions on Neural Networks , vol. 22, no. 2, pp. 199–210, 2011
2011
Earlier work this paper cites.
C. Cortes and M. Mohri, “Domain adaptation in regression,” in International Conference on Algorithmic Learning Theory . Springer, 2011, pp. 308–323
2011
Earlier work this paper cites.
P. Tamilselvan and P. Wang, “Failure diagnosis using deep belief learning based health state classification,” Reliability Engineering & System Safety , vol. 115, pp. 124–135, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. E. Vásquez, “Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis,” Neurocomputing , vol. 168, pp. 119–127, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
W. A. Smith and R. B. Randall, “Rolling element bearing diagnostics using the case western reserve university data: A benchmark study,” Mechanical Systems and Signal Processing , vol. 64, pp. 100–131, 2015
2015
Cited alongside, same era.
2016
Cited alongside, same era.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7167–7176
2017
Later among the works it cites.
K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy for unsupervised domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3723–3732
2018
Later among the works it cites.
2018
Later among the works it cites.
X. Li, W. Zhang, and Q. Ding, “Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks,” IEEE Transactions on Industrial Electronics , 2018
2018
Later among the works it cites.
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2016
Cited alongside, same era.
2016
Cited alongside, same era.
F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data,” Mechanical Systems and Signal Processing , vol. 72, pp. 303–315, 2016
2016
Cited alongside, same era.
W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang, “A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals,” Sensors , vol. 17, no. 2, p. 425, 2017
2017
Cited alongside, same era.
W. Zhang, C. Li, G. Peng, Y. Chen, and Z. Zhang, “A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load,” Mechanical Systems and Signal Processing , vol. 100, pp. 439–453, 2018
2018
Later among the works it cites.
X. Li, W. Zhang, Q. Ding, and J.-Q. Sun, “Multi-layer domain adaptation method for rolling bearing fault diagnosis,” Signal Processing , vol. 157, pp. 180–197, 2019
2019
Closest in time.
G. Michau and O. Fink, “Unsupervised Fault Detection in Varying Operating Conditions,” in Proceedings of the 2019 IEEE International Conference on Prognostics and Health Management , 2019
2019
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