  In the era of industrial big data, prognostics and health management is
essential to improve the prediction of future failures to minimize inventory,
maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new
Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an
open-source benchmark containing simulated turbofan engine units flown under
realistic flight conditions. Deep learning approaches implemented previously
for this application attempt to predict the remaining useful life of the engine
units, but have not utilized labeled failure mode information, impeding
practical usage and explainability. To address these limitations, a new
prognostics approach is formulated with a customized loss function to
simultaneously predict the current health state, the eventual failing
component(s), and the remaining useful life. The proposed method incorporates
principal component analysis to orthogonalize statistical time-domain features,
which are inputs into supervised regressors such as random forests, extreme
random forests, XGBoost, and artificial neural networks. The highest performing
algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each
classification. In addition, ANN-Flux reduces the remaining useful life RMSE by
38% for the same test split of the dataset compared to past work, with
significantly less computational cost.
