| Citation: | Zhang Chenchen, Pan Muxuan. Modeling approach for turbofan engines based on intelligent multi-feature extraction and interval type-2 fuzzy sets[J]. Journal of Aerospace Power, 2026, 41（8）：20240753 doi: 10.13224/j.cnki.jasp.20240753
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Considering turbofan engine’s strong nonlinearity and significant uncertainty within wide operational ranges, a new modeling approach for turbofan engines in the full flight envelope was proposed based on multi-feature extraction and interval type-2 (IT2) fuzzy sets. The engine multi-feature parameters were designed and extracted. To avoid the failure of traditional clustering algorithms in high-dimensional spaces, an improved discriminant neighborhood embedding algorithm (IDNE) was developed for dimensionality reduction of multi-feature parameters and collaborated with the fuzzy C-means algorithm (FCM) to extract the typical features. The consequent models were identified at typical feature points. The IT2 membership functions were optimized to improve the accuracy of the fuzzy model under uncertainty. Finally, an IT2 fuzzy model for a low bypass ratio turbofan engine was established. Model performance within the flight envelope was validated. The results showed that the model had high accuracy as the average root mean square error (ARMSE) was less than 0.20%. Under degradation uncertainties, the IT2 fuzzy model’s accuracy variation was less than 0.05%, significantly better than the type-1 fuzzy model, which showed a better uncertainty representation capability. The resulting model demonstrated an excellent real-time performance with around 3.9 ms for its average computation time per instance.
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