Reconstructing secondary test database from PHM08 challenge data set

Abstract

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).

A neural network framework for similarity-based prognostics

“…Before the results of the presented algorithm are sent for validation, the model is tested by reconstructed secondary test datasets which were randomly selected from full size training trajectories [13]. The length of units in reconstructed database is equal to the final test units from the original file to ensure that similar behavior of test trajectories is obtained.…”

Two New Shellear Species (Gonorhynchiformes: Kneriidae), from the Luansa River (Upper Congo Basin): Hidden Diversity Revealed by Integrative Taxonomy

“…The use of K2P-and uncorrected p-distances were necessary as K2P-distances tend to exaggerate species delimitation based on closely related sequences, whereas the p-distance does not account for multiple mutation hits in strongly divergent sequences (see [52]). Nevertheless, since both distance values are routinely reported in DNA-barcoding studies, both are presented to enable direct comparison with other studies (see also [53,54]). The number of base substitutions per site averaged over all sequence pairs within each population and between sequences is shown.…”

Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives

“…When one considers the computational prognostic algorithms under dynamic regimes, special attention is given to the multidimension type data due to the operational differences between the regimes [20]. In these complex cases, the systems are formed by various interacting components and the common system behavior cannot be easily deduced from individual elements so that the predictability of system is limited and the responses do not scale linearly [21].…”