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).
[1]
Oguz Bektas.
An adaptive data filtering model for remaining useful life estimation
,
2018
.
[2]
Abhinav Saxena,et al.
Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets
,
2020,
International Journal of Prognostics and Health Management.
[3]
Hai Qiu,et al.
Modeling Propagation of Gas Path Damage
,
2007,
2007 IEEE Aerospace Conference.
[4]
Abhinav Saxena,et al.
Damage propagation modeling for aircraft engine run-to-failure simulation
,
2008,
2008 International Conference on Prognostics and Health Management.
[5]
Jeffrey Alun Jones,et al.
NARX time series model for remaining useful life estimation of gas turbine engines
,
2016
.
[6]
Jonathan S. Litt,et al.
User's Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS)
,
2007
.
[7]
Kai Goebel,et al.
A neural network filtering approach for similarity-based remaining useful life estimation
,
2018,
The International Journal of Advanced Manufacturing Technology.
[8]
Jeffrey Alun Jones,et al.
Reducing Dimensionality of Multi-regime Data for Failure Prognostics
,
2017,
Journal of Failure Analysis and Prevention.