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Reconstructing secondary test database from PHM08 challenge data set

机译:从PHM08挑战数据集重建二级测试数据库

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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).
机译:在此数据文章中,提供了一个重建的数据库,该数据库提供了来自PHM08挑战数据集的信息。原始涡扇发动机数据来自NASA Ames研究中心的卓越预测中心(PCoE)(Saxena and Goebel,2008),并由商业模块化航空推进系统仿真(C-MAPSS)进行了仿真(Saxena等。 ,2008)。数据集进一步分为“训练”,“测试”和“最终测试”子集。期望合作者使用“训练”数据子集来训练他们的模型,评估“测试”子集上的剩余使用寿命(RUL)预测性能,最后将模型应用于“最终测试”子集进行竞争。但是,“最终测试”结果只能通过电子邮件发送给PCoE一次。在将结果发送给性能评估之前,为了使用真实的RUL值对数据集进行预验证,此数据文章介绍了从原始轨迹的嘈杂降级模式得出的重构二级数据集。重建数据库是指从训练轨迹中收集的数据。从根本上说,它由单独的部分轨迹组成,其中RUL被称为基本事实。它的使用为针对PHM08数据挑战开发的模型提供了可靠的验证,否则,由于一次性提交的高风险,该模型可能会模棱两可。这些数据和分析支持研究数据文章“基于相似性的剩余使用寿命估计的神经网络过滤方法”(Bektas等人,2018)。

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