Machine Learning for Failure Analysis: A Mathematical Modelling Perspective

Author:

Pérez-Velázquez Judith,Gölgeli Meltem,Ruiz Guido Carlos Alfonso

Publisher

Springer International Publishing

Reference19 articles.

1. O. Addin, S.M. Sapuan, E. Mahdi, M. Othman, A Naïve-Bayes classifier for damage detection in engineering materials. Mater. Des. 28(8), 2379–2386 (2007)

2. C.A. Azencott, Introduction au Machine Learning, Dunod (2019).

3. S. Biswas, D. Fernandez Castellanos, M. Zaiser, Prediction of creep failure time using machine learning. Sci. Rep. 10(1), 16910 (2020)

4. J. Feldman, R. Rojas, Neural Networks: A Systematic Introduction (Springer, Berlin, 2013)

5. N. Günnemann, J. Pfeffer, Predicting defective engines using convolutional neural networks on temporal vibration signals, in Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, ed. by L. Torgo, B. Krawczyk, P. Branco, N. Moniz, volume 74 of Proceedings of Machine Learning Research, ECML-PKDD, Skopje, Macedonia, 22 Sep 2017. PMLR (2017), pp. 92–102

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