SVĚTOVÝ, EVROPSKÝ A ČESKÝ AUTOMOBILOVÝ PRŮMYSL A TRH S AUTOMOBILY :: Šaroch a kol.

Prameny Atamuradov, V.; Medjaher, K.; Dersin, P.; Lamoureux, B.; Zerhouni, N. Prognostics and Health Management for Maintenance Practitioners – Review, Implementation and Tools Evaluation. Int J Progn Health Manag 2017, 8, 1–31. Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. A Tutorial on Particle Filters for Online Nonlinear/ Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing . 2002, 50(2), 174–188. Baraldi, P.; Di Maio, F.; Zio, E. Particle filters for prognostics. Prognostics and health management society. European Conference of the Prognostics and Health Management Society, Nantes, France. 2014. Baraldi, P.; Mangili, F.; Zio, E. Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data. Reliability Engineering and System Safety.2013, (112), 94–108. Baraldi, P.; Popescu, I. C.; Zio, E. Methods of Uncertainty Analysis in Prognostics. 2012. Barbera, F.; Schneider, H.; Kelle, P.: A Condition Based Maintenance Model with Exponential Failures and Fixed Inspection Intervals. The Journal of the Operational Research Society , August 1996, Vol. 47, No. 8, pp. 1037–1045. Bender, A.; Sextro, W. Hybrid Prognosis Method for Remaining Useful Lifetime Estimation Considering Uncertainties. Proceedings of the 6th European Conference of the Prognostics and Health Management Society 2021 . ISBN 978-1-936263-34-9 Bender, A. Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall Elementen unter Berücksichtigung systembasierter Unsicherheiten (Dissertation). 2021. Universität Paderborn, Paderborn. Retrieved from https://katalog.ub.uni-paderborn.de/ local/records/002214985. Besnard, F.; Bertling, L.: An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades . IEEE Transactions on Sustainable Energy, July 2010, Vol. 1, Issue 2, pp. 77–83, ISSN 1949-3029. Bishop, C. M., Nasrabadi, N. M. Pattern Recognition and Machine Learning. 1. vyd. Berlin: Springer, 2019. Cachada, A.; Moreira, P.M.; Romero, L.; Barbosa, J.; Leitno, P.; Gcraldcs, C.A.S.; Deusdado, L.; Costa, J.; Teixeira, C.; Teixeira, J.; et al. Maintenance 4.0: Intelligent and Predictive Maintenance System Architecture. In Proceedings of the 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA ); IEEE, 2018; pp. 139–146. Carvalho, T.P.; Soares, F.A.A.M.N.; Vita, R.; Francisco, R. da P.; Basto, J.P.; Alcalá, S.G.S. A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 2019, 137, 106024, DOI: 10.1016/j.cie.2019.106024. ČSN EN 13306 (010660): 2018 Údržba – Terminologie údržby.

204