Research Article
Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines
Table 7
Forecasting errors using feature selection.
| Training | RF | XGB | SVM | DNN | RNN | LSTM | GRU | CNN |
| RMSE | 19.70 | 19.3 | 21.25 | 21.04 | 22.15 | 22.17 | 22.07 | 21.84 | MAE | 14.77 | 14.72 | 13.49 | 16.39 | 17.69 | 17.95 | 17.83 | 17.37 | MAAPE | 23.17 | 22.15 | 21.57 | 23.32 | 24.17 | 24.26 | 24.19 | 23.6 | Testing | RF | XGB | SVM | DNN | RNN | LSTM | GRU | CNN | RMSE | 19.79 | 19.33 | 22.91 | 19.15 | 19.32 | 19.22 | 18.93 | 18.85 | MAE | 14.46 | 14.1 | 16.69 | 14.27 | 14.3 | 14.38 | 14.13 | 14.36 | MAAPE | 25.84 | 25.35 | 27.93 | 24.61 | 23.89 | 24.53 | 23.85 | 23.89 |
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