Course information, Prognostics, 8(?) credits
Vehicular Systems, 2014-2015
Course objectives
Examination
Exercises: exercises, (vor_edit_R.csv, simdata.csv, simpred.csv)
Course plan
Course material
- Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press. (Chapters 1-6)
- Cox, D. R., & Oakes, D. (1984). Analysis of survival data (Vol. 21). CRC Press. (Chapters 1-5)
- Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
- Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32
- Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The Annals of Applied Statistics, 841-860. Chicago
- Daigle, M.J.; Goebel, K., Model-Based Prognostics With Concurrent Damage Progression Processes. Systems, Man, and Cybernetics: Systems, IEEE Transactions on , vol.43, no.3, pp.535,546, May 2013 doi: 10.1109/TSMCA.2012.2207109
- Tutorial at PHM Conference 2014: Model-based Prognostics. Presented by: Dr. Matthew Daigle, Senior Researcher, Prognostics Center of Excellence, Intelligent Systems Division, NASA Ames Research Center
- A. Saxena, j. Celaya, B. Saha, S. Saha, and K. Goebel. Metrics for Offline Evaluation of Prognostic Performance. International Journal of Prognostics and Health Management, vol. 1, no. 1, 2010.
- Orchard, M., & Vachtsevanos, G. (2009). A particle-filtering approach for on-line fault diagnosis and failure prognosis.. Transactions Of The Institute Of Measurement And Control, 31(3), 221-246. doi:10.1177/0142331208092026
- Tobon-Mejia, Diego Alejandro, et al. "A data-driven failure prognostics method based on mixture of gaussians hidden markov models." Reliability, IEEE Transactions on 61.2 (2012): 491-503.
- Chen, Nan, and Kwok Leung Tsui. "Condition monitoring and remaining useful life prediction using degradation signals: Revisited." IIE Transactions 45.9 (2013): 939-952.
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Last updated: 2015-03-04