Reconstructing secondary test database from PHM08 challenge data set
| Autor: | Shankar Sankararaman, Oguz Bektas, Kai Goebel, Jeffrey Alun Jones, Indranil Roychoudhury |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Annan samhällsbyggnadsteknik
Similarity (geometry) Computer science 020209 energy Center of excellence 02 engineering and technology PHM08 challenge data set computer.software_genre lcsh:Computer applications to medicine. Medical informatics Data-driven prognostics Engineering 0203 mechanical engineering C-MAPPS datasets 0202 electrical engineering electronic engineering information engineering lcsh:Science (General) Ground truth Multidisciplinary Database Artificial neural network business.industry Modular design Other Civil Engineering Test (assessment) Data set 020303 mechanical engineering & transports lcsh:R858-859.7 Commercial modular aero-propulsion system simulation business computer Degradation (telecommunications) lcsh:Q1-390 |
| Zdroj: | Data in Brief, Vol 21, Iss, Pp 2464-2469 (2018) Data in Brief |
| Popis: | In this data article, a reconstructed database, which provides information from PHM08 challenge data set, is presented. The original turbofan engine data were from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center (Saxena and Goebel, 2008), and were simulated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (Saxena et al., 2008). The data set is further divided into ''training'', ''test'' and ''final test'' subsets. It is expected from collaborators to train their models using “training” data subset, evaluate the Remaining Useful Life (RUL) prediction performance on “test” subset and finally, apply the models to the “final test” subset for competition. However, the ''final test'' results can only be submitted once by email to PCoE. Before the results are sent for performance evaluation, in order to pre-validate the dataset with true RUL values, this data article introduces reconstructed secondary datasets derived from the noisy degradation patterns of original trajectories. Reconstructed database refers to data that were collected from the training trajectories. Fundamentally, it is formed of individual partial trajectories in which the RUL is known as a ground truth. Its use provides a robust validation of the model developed for the PHM08 data challenge that would otherwise be ambiguous due to the high-risk of one-time submission. These data and analyses support the research data article “A Neural Network Filtering Approach for Similarity-Based Remaining Useful Life Estimations” (Bektas et al., 2018). Keywords: Commercial modular aero-propulsion system simulation, C-MAPPS datasets, PHM08 challenge data set, Data-driven prognostics |
| Databáze: | OpenAIRE |
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