-
1. Fidali M.: Metody diagnostyki maszyn i urządzeń w predykcyjnym utrzymaniu ruchu. Elamed Media Group, Katowice 2020.
-
2. Jimenez-Cortadi A., Irigoien I., Boto F., Sierra B., Rodriguez G.: Predictive Maintenance on the Machining Process and Machine Tool. „Applied Sciences”, 10/2020, s. 224.
-
3. Mulewicz B., Marzec M., Morkisz P., Oprocha P.: Failures Prediction Based on Performance Monitoring of a Gas Turbine: a Binary Classification Approach. „Schedae Informaticae”, 1/2017, s. 9-21.
-
4. Theissler A., Pérez-Velázquez J., Kettelgerdes M., Elger G.: Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. „Reliability Engineering & System Safety”, 11/2021.
-
5. https://www.mathworks.com/company/newsletters/articles/three-ways-to-estimate-remaining-useful-life-for-predictive-maintenance.html [dostęp: 13.02.2022 r.].
-
6. Analityka predykcyjna w eksploatacji maszyn. ReliaSol, 2020.
-
7. Çınar Z.M., Abdussalam Nuhu A., Zeeshan Q., Korhan O., Asmael M., Safaei B.: Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. „Sustainability”, 1/2020.
-
8. Daniyan I., Mpofu K., Oyesola M., Ramatsetse B., Adeodu A.: Artificial intelligence for predictive maintenance in the railcar learning factories. „Procedia Manufacturing”, 4/2020, s. 13-18.
-
9. Boylu Uz F.: Deep learning for predictive maintenance with Long Short Term Memory Networks https://azure.microsoft.com/pl-pl/blog/deep-learning-for-predictive-maintenance [dostęp: 15.02.2022 r.].
-
10. https://granteam.pl/pl/oferta/pdm [dostęp: 16.02.2020 r.].