Synthetic Data for Smarter RUL Prediction
Deep Generative Models in Turbofan Analysis
D.C. Saadeldin (TU Delft - Aerospace Engineering)
I.I. de Pater – Mentor (TU Delft - Operations & Environment)
Marcia L. Baptista – Mentor (TU Delft - Air Transport & Operations)
J Ellerbroek – Graduation committee member (TU Delft - Operations & Environment)
O.A. Sharpans'kykh – Graduation committee member (TU Delft - Operations & Environment)
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Abstract
Scarce failure data often causes unreliable results when making predictions concerning Remaining Useful Life (RUL). This study explores the use of deep generative models (DGMs) for augmenting turbofan engine datasets by CMAPSS to improve these RUL predictions. By implementing Conditional Tabular GANs (CTGAN) and Tabular Variational Autoencoders (TVAE), synthetic data is generated and validated using statistical metrics such as Wasserstein distance and Kolmogorov-Smirnov tests. Then, these new datasets are used in several compositions of both real and synthetic data to train regressors and subsequently let them make RUL predictions. The regressors, such as Random Forest Regressors (RFR) and Convolutional Neural Networks (CNN), evaluate performance improvements through RMSE and MAE metrics. Results indicate that adding synthetic data improves prediction robustness, particularly when data is limited. This highlights the potential of DGMs for Prognostics and Health Management (PHM) applications.