1. An, D., Kim, N., & Choi, J. (2014). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering and System Safety, 133, 223–236. https://doi.org/10.1016/j.ress.2014.09.014.
2. Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners – review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 2017(060). https://www.semanticscholar.org/paper/Prognostics-and-health-management-for-maintenance-Atamuradov-Medjaher/e4c6211ab54ae83c7f1763f37451bf8e1b130dcb.
3. Bartram, G.W. (2013). System Health Diagnosis And Prognosis Using Dynamic Bayesian Networks. Ph.D. Thesis, Vanderbilt University, Nashville.
4. Borutzky, W. (Ed.). (2016). Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems (2nd ed.). Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-47434-2.
5. Borutzky, W. (2018). Determination of a function for a degradation process by means of two diagnostic bond graphs. In Proceedings of the 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, IFAC, Warsaw.