[9] Selcuk S. Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2017:231(9):1670–1679. https://doi.org/10.1177/095440541560164010.1177/0954405415601640
[10] Sharma A. PyTorch on Azure: Deep learning in the oil and gas industry [Online]. [Accessed 23.01.2020]. Available: https://azure.microsoft.com/en-us/blog/pytorch-on-azure-deep-learning-in-the-oil-and-gas-industry/?cdn=disable
[11] UNITED NATIONS. About the Sustainable Development Goals - United Nations Sustainable Development [Online]. [Accessed 19.02.2020]. Available: https://www.un.org/sustainabledevelopment/sustainable-development-goals
[15] Golinska P., Fertsch M., Marx-Gómez J. Information Technologies in Environmental Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.10.1007/978-3-642-19536-5
[16] Jasiulewicz-Kaczmarek M., Drozyner P. Maintenance Management Initiatives Towards Achieving Sustainable Development. In: Golinska P., Fertsch M., Marx-Gómez J. (eds) Information Technologies in Environmental Engineering. Environmental Science and Engineering, vol. 3. Springer, Berlin, Heidelberg.
[18] United States Environmental Protection Agency. Equipment Upgrades and Preventive Maintenance Improve Performance and Reduce SF6 Emissions. US: EPA, 2017.
[21] AGFW. FW 114 - Instandhaltungsstrategien und Rehabilitationsplanung – Mindestanforderungen. (Maintenance strategies and rehabilitation planning-minimum requirements.) Frankfurt am Main: AGFW, 2013 (in German) Lund H., et al. 4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems. Energy 2014:68:1–11. https://doi.org/10.1016/j.energy.2014.02.08910.1016/j.energy.2014.02.089
[23] Pourbozorgi Langroudi P., Weidlich I. Entwicklung von neuen und verbesserten Instandhaltungsstrategien für kleine und große Wärmeverteilnetze durch Kombination statistischer Alterungsmodelle mit materialbasierten Nutzungsdauermodellen: AP1. (Development of new and improved maintenance strategies for small and large heat distribution networks by combining statistical aging models with material-based service life models: AP1.) unpublished intermediate report, project number 03ET1625B. Berlin: Federal Ministry for Economic Affairs and Energy of Germany, 2019. (in German)
[28] Raschka S. Python machine learning: Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics. Birmingham, Mumbai: Packt Publishing open source, 2016.
[30] Brunton S. L., Kutz N. J. Data-driven science and engineering: Machine learning, dynamical systems and control. Cambridge: Cambridge University Press, 2019.10.1017/9781108380690
[31] Brunton S. Intro to Data Science: Types of Machine Learning 2 [Online]. [Accessed 17.02.2020]. Available: https://www.youtube.com/watch?v=0_lKUPYEYyY&list=PLMrJAkhIeNNQV7wi9r7Kut8liLFMWQOXn&index=10
[34] Krizhevsky A., et al. Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 2012:25(2):1097–1105.
[36] van Veen F. The Neural Network Zoo. The Asimov Institute, 2016. [Online]. [Accessed 18.02.2020]. Available: https://www.asimovinstitute.org/neural-network-zoo
[38] Brownlee J. Deep Learning With Python: Develop Deep Learning Models On Theano And TensorFlow Using Keras. Vermont Victoria: Machine Learning Mastery, 2016.
[42] Sharma A. PyTorch on Azure: Deep learning in the oil and gas industry [Online]. [Accessed 30.10.2019]. Available: https://azure.microsoft.com/en-us/blog/pytorch-on-azure-deep-learning-in-the-oil-and-gas-industry/?cdn=disable