A Singular Spectrum Analysis-based Synthetic Dataset Generation Method for Remaining Useful Life Estimation of Turbo Fan Engines

Peerapol Yuvapoositanon, Mahanakorn University of Technology, Prakit Intachai, Phetchaburi Rajabhat University
2021 International Journal of Intelligent Engineering and Systems  
In this paper, we propose a novel method of generating synthetic datasets by means of singular spectrum analysis (SSA) with the optimal window length for substituting the actual datasets that are needed for remaining useful life (RUL) estimation of turbofan engines. The validity of proposed method is confirmed by testing with 200 actual datasets from turbofan engine datasets and 200 synthetic datasets generated by the proposed method in comparison to those generated by three algorithms: the
more » ... ier Decomposition Method ( FDM), the Fast Fourier Transform (FFT) and the Empirical Mode Decomposition (EMD). The performance of the SSA-based synthetic datasets for RUL estimation was compared with those of the FFT, EMD and FDM algorithms by means of the regression performed by the Long Short Term Memory (LSTM) neural networks. All the results were measured in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of their RUL estimates averaged over 200 datasets. The results were also compared with those of the actual feature dataset which provided the MAE of 23.828 and RMSE of 35.284. For the synthetic datasets, the results showed the MAE of 27.126 and RMSE of 38.472 for the FFT, the MAE of 28.362 and RMSE of 39.402 for the EMD and the MAE of 30.410 and RMSE of 41.705 for the FDM. It was revealed that the synthetic datasets generated by the proposed SSA-based method performed the best with the MAE of 25.123 and RMSE of 36.825 confirming the applicability of the proposed SSA-based synthetic datasets in substitution of the actual datasets for RUL estimation.
doi:10.22266/ijies2021.0831.32 fatcat:2cxgtbcfobcr3ez35k2fmfmawm