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Reconstructing secondary test database from PHM08 challenge data set
Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, United Kingdom.
Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, United Kingdom.
PricewaterhouseCoopers, San Jose, CA 95110, United States.
Stinger Ghaffarian Technologies, Inc., NASA Ames Research Center, Moffett Field, CA 94035, United States.
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2018 (Engelska)Ingår i: Data in Brief, E-ISSN 2352-3409, Vol. 21, s. 2464-2469Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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).

Ort, förlag, år, upplaga, sidor
Elsevier, 2018. Vol. 21, s. 2464-2469
Nyckelord [en]
Commercial modular aero-propulsion system simulation, C-MAPPS datasets, PHM08 challenge data set, Data-driven prognostics
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URN: urn:nbn:se:ltu:diva-71982DOI: 10.1016/j.dib.2018.11.085ISI: 000457925900336Scopus ID: 2-s2.0-85057839272OAI: oai:DiVA.org:ltu-71982DiVA, id: diva2:1269322
Tillgänglig från: 2018-12-10 Skapad: 2018-12-10 Senast uppdaterad: 2019-03-08Bibliografiskt granskad

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