Uses the dataset from [1] to create a demostration of a machine learning setup for a predictive maintainance scenario for Turbofan Engines.
References:
- A. Saxena, K. Goebel, D. Simon, and N. Eklund, "Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation", in the Proceedings of the Ist International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008., retrieved feb. 2016
- NASA Ames Prognostics data repository, retrieved feb. 2016, http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/
- Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets, O. F. Eker1, F. Camci, and I. K. Jennions1, retrieved feb. 2016
- Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams. / Zhang, Liangwei. Luleå : Luleå tekniska universitet, 2015. 46 p. (Licentiate thesis / Luleå University of Technology). Publication: Research › Licentiate thesis, retrieved feb. 2016
- Microsoft Cortana example with the same dataset, retrieved feb. 2016 Link
- H2o.io example with the same dataset, retrieved feb. 2016 Link Presentation
- Advanced Analytics with Spark - Patterns for Learning from Data at Scale By Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills. Link Examples
- The use of the area under the ROC curve in the evaluation of machine learning algorithms,Andrew P Bradley Link
- A Few Useful Things to Know about Machine Learning, Pedro Domingos, Link