{"url":"/dataset/nasa-c-mapss-2","name":"NASA C-MAPSS-2","full_name":"Turbofan Engine Degradation Simulation Data Set-2","description_markdown":"The generation of data-driven prognostics models requires the availability of datasets with run-to-failure trajectories. In order to contribute to the development of these methods, the dataset provides a new realistic dataset of run-to-failure trajectories for a small fleet of aircraft engines under realistic flight conditions. The damage propagation modelling used for the generation of this synthetic dataset builds on the modeling strategy from previous work . The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model. The data set is been provided by the Prognostics CoE at NASA Ames in collaboration with ETH Zurich and PARC.","description_withheld":null,"homepage":"https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan-2","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Remaining Useful Lifetime Estimation","url":"/task/remaining-useful-lifetime-estimation","datasets_with_task":"/datasets/task/remaining-useful-lifetime-estimation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NASA C-MAPSS-2"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/remaining-useful-lifetime-estimation-on-nasa","task":"Remaining Useful Lifetime Estimation","dataset_variant":"NASA C-MAPSS-2","rows":1,"metrics":["Score"],"first_row_in_archive_order":{"model":"Stacked DCNN","paper":"/paper/a-stacked-deep-convolutional-neural-network","metrics":{"Score":"3.651"},"code_links":[{"title":"datrikintelligence/stacked-dcnn-rul-phm21","url":"https://github.com/datrikintelligence/stacked-dcnn-rul-phm21"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-stacked-deep-convolutional-neural-network","title":"A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine","date":"2021-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}