Abstract
The Industry 4.0 revolution allows monitoring and intelligent processing of big amounts of data. When monitoring certain assets, very few data is found for operation under faulty conditions because the cost of not operating properly is unacceptable and thus preventive strategies are put in practice. Because machine learning algorithms are data exhaustive, synthetic data can be created for these cases. Deep learning techniques have been proven to work very well for these cases. Generative Adversarial Networks (GANs) have been deployed in numerous applications with data augmentation objectives, but not so much for balancing unidimensional series with few data. In this paper, a GAN is applied in order to augment data for assets operating under faulty conditions. The proposed method is validated on a real industrial case, yielding promising results with respect to the case with no strategy for class imbalance whatsoever.
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References
Beyan, C., Fisher, R.: Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recogn. 48(5), 1653–1672 (2015)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(9), 321–357 (2002)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)
Diez-Olivan, A., Del Ser, J., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards industry 4.0. Inf. Fus. 50, 92–111 (2019)
Diez-Olivan, A., Penalva, M., Veiga, F., Deitert, L., Sanz, R., Sierra, B.: Kernel density-based pattern classification in blind fasteners installation. In: Martínez de Pisón, F.J., Urraca, R., Quintián, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 195–206. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59650-1_17
Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc. (2014). http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
Gui, J., Sun, Z., Wen, Y., Tao, D., Ye, J.: A review on generative adversarial networks: Algorithms, theory, and applications (2020)
He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks, pp. 1322–1328 (2008)
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Jiang, W., Hong, Y., Zhou, B., He, X., Cheng, C.: A GAN-based anomaly detection approach for imbalanced industrial time series. IEEE Access 7, 143608–143619 (2019)
Lee, T., Lee, K.B., Kim, C.O.: Performance of machine learning algorithms for class-imbalanced process fault detection problems. IEEE Trans. Semicond. Manuf. 29(4), 436–445 (2016)
Madhu, A., Kumaraswamy, S.: Data augmentation using generative adversarial network for environmental sound classification. In: European Signal Processing Conference, pp. 1–5 (2019)
Mehta, K., Kobti, Z., Pfaff, K., Fox, S.: Data augmentation using CA evolved GANs. In: IEEE Symposium on Computers and Communications, pp. 1087–1092 (2019)
Oh, J.H., Hong, J.Y., Baek, J.G.: Oversampling method using outlier detectable generative adversarial network. Expert Syst. Appl. 133, 1–8 (2019)
Ortego, P., Diez-Olivan, A., Del Ser, J., Veiga, F., Penalva, M., Sierra, B.: Evolutionary LSTM-FCN networks for pattern classification in industrial processes. Swarm Evol. Comput. 54, 100650 (2020). https://doi.org/10.1016/j.swevo.2020.100650
Shao, S., Wang, P., Yan, R.: Generative adversarial networks for data augmentation in machine fault diagnosis. Comput. Ind. 106, 85–93 (2019)
Sharma, A., Jindal, N., Thakur, A.: Comparison on generative adversarial networks - a study. In: International Conference on Secure Cyber Computing and Communication, pp. 391–396 (2018)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019)
Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., Wang, F.: Generative adversarial networks: introduction and outlook. IEEE/CAA J. Automatica Sinica 4(4), 588–598 (2017)
Xie, Y., Zhang, T.: Imbalanced learning for fault diagnosis problem of rotating machinery based on generative adversarial networks. In: 2018 37th Chinese Control Conference (CCC), pp. 6017–6022, July 2018
Acknowledgments
This project was supported by the Spanish Centro para el Desarrollo Tecnologico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project), as well as by the Basque Government through EMAITEK and ELKARTEK (ref. KK-2020/00049) funding grants. J. Del Ser also acknowledges support from the Department of Education of the Basque Government (Consolidated Research Group MATHMODE, IT1294-19).
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Ortego, P., Diez-Olivan, A., Del Ser, J., Sierra, B. (2020). Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_11
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