An I4.0 data intensive platform suitable for the deployment of machine learning models: a predictive maintenance service case study

Author:

Herrero Ricardo Dintén,Zorrilla Marta

Publisher

Elsevier BV

Subject

General Engineering

Reference14 articles.

1. Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data;Berthold,2010

2. Canizo, M., Onieva, E., Conde, A., Charramendieta, S., Trujillo, S., 2017. Real-time predictive maintenance for wind turbines using big data frameworks, in: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 70–77. doi:10.1109/ICPHM. 2017.7998308.

3. Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection, in: Proceedings of the 14th International Joint Conference on Artificial Intelligence-Volume 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. p. 1137–1143.

4. Kohavi, R., 2001. A study of cross-validation and bootstrap for accuracy estimation and model selection 14.

5. Improving rail network velocity: A machine learning approach to predictive maintenance;Li;Transportation Research Part C: Emerging Technologies,2014

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