International Journal of Aerospace Engineering / 2024 / Article / Tab 3 / Research Article
Aeroengine Remaining Life Prediction Using Feature Selection and Improved SE Blocks Table 3 Comparison with other state-of-the-art methods on the CMPASS dataset.
Dataset FD001 FD002 FD003 FD004 Evaluation RMSE Score RMSE Score RMSE Score RMSE Score Hybrid [16 ] 14.53 322.4 NA NA NA NA 27.08 5649.1 DCNN [8 ] 12.61 274 22.36 10412 12.64 284 23.31 12466 MOBNE [32 ] 15.04 334 25.05 5585 12.51 422 28.66 6558 Deep LSTM [17 ] 16.14 338.0 24.49 4450.0 16.18 852.0 28.17 5550.0 BiLSTM [33 ] 13.65 261 23.18 4130 12.74 317 24.86 5430 SBRNN [34 ] 13.58 228 19.59 2650 19.16 1727 22.15 2901 BiGRU [35 ] 12.65 213 18.9 2264 12.5 233 20.5 3610 Tafcn [12 ] 13.99 336 17.06 1946 12.01 251 19.79 3671 Our method 13.46 265.9 16.54 1465.1 11.79 222.0 19.39 2036.2
The bold contents represent the optimal experimental results. This table shows excellent performance by comparing the results of the multiple research methods and comparing our method with other state-of-the-art methods.