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Reconstructing secondary test database from PHM08 challenge data set

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...

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Autores principales: Bektas, Oguz, Jones, Jeffrey A., Sankararaman, Shankar, Roychoudhury, Indranil, Goebel, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288980/
https://www.ncbi.nlm.nih.gov/pubmed/30560154
http://dx.doi.org/10.1016/j.dib.2018.11.085
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author Bektas, Oguz
Jones, Jeffrey A.
Sankararaman, Shankar
Roychoudhury, Indranil
Goebel, Kai
author_facet Bektas, Oguz
Jones, Jeffrey A.
Sankararaman, Shankar
Roychoudhury, Indranil
Goebel, Kai
author_sort Bektas, Oguz
collection PubMed
description 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).
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spelling pubmed-62889802018-12-17 Reconstructing secondary test database from PHM08 challenge data set Bektas, Oguz Jones, Jeffrey A. Sankararaman, Shankar Roychoudhury, Indranil Goebel, Kai Data Brief Engineering 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). Elsevier 2018-11-20 /pmc/articles/PMC6288980/ /pubmed/30560154 http://dx.doi.org/10.1016/j.dib.2018.11.085 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Engineering
Bektas, Oguz
Jones, Jeffrey A.
Sankararaman, Shankar
Roychoudhury, Indranil
Goebel, Kai
Reconstructing secondary test database from PHM08 challenge data set
title Reconstructing secondary test database from PHM08 challenge data set
title_full Reconstructing secondary test database from PHM08 challenge data set
title_fullStr Reconstructing secondary test database from PHM08 challenge data set
title_full_unstemmed Reconstructing secondary test database from PHM08 challenge data set
title_short Reconstructing secondary test database from PHM08 challenge data set
title_sort reconstructing secondary test database from phm08 challenge data set
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6288980/
https://www.ncbi.nlm.nih.gov/pubmed/30560154
http://dx.doi.org/10.1016/j.dib.2018.11.085
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