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<p>Aircraft turbofan engines undergo gradual performance degradation that, if left undetected, can compromise flight safety and increase maintenance costs. In this study, we propose a heterogeneous ensemble deep neural network (HEDNN) for multi-class health status prediction of real-life turbofan engines. Our methodology begins with the collection of 43,492 real operational records (2012–2024) from a dual-spool, variable geometry turbofan, each run sampled at a fixed throttle setting. We preprocess raw sensor outputs using a robust z-score (median absolute deviation) filter to remove outliers, apply a 35-sample moving median imputation to address 1,812 missing temperature entries, and eliminate any non-numeric artifacts. The cleaned dataset consisting of 97 predictive features after FADEC upgrade harmonization is partitioned into training (80%, <i>n</i> = 34 794) and testing (20%, <i>n</i> = 8 698) sets, and labeled into three health classes (no/low degradation, mid-life degradation, and advanced degradation) built upon performance margin (PMAR) and specific fuel consumption (SMAR) indices.</p><p>We engineer three base learners: bidirectional LSTM (128 hidden units), one-dimensional CNN (11 convolution normalization ELU blocks), and BiGRU (four GRU layers, custom flip/concatenate stages), each trained for up to 60 epochs, batch size 16, using the Adam optimizer with piecewise learning rate decay (initial <i>η</i> = 0.01). Individual networks produce class probability vectors that are fused in a shallow fully connected ensemble, finalized by softmax classification.</p><p>Performance is assessed via RMSE, MAPE, overall accuracy, precision, recall, F1 score, and multi-class AUC. The HEDNN achieves RMSE = 0.3421 (33–41% lower than any single model), MAPE = 4.78% (60–65% reduction), and accuracy = 95.32%, with F1 = 0.928 and mean AUC = 0.777. In contrast, the best individual learner (BiLSTM) reaches RMSE = 0.5149, MAPE = 12.17%, accuracy = 82.44%, and AUC = 0.717. These results demonstrate that our heterogeneous ensemble markedly outperforms standalone architectures, offering a robust, data-driven prognostic tool for predictive maintenance of turbofan fleets.</p>

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Turbofan engine health status prediction with heterogeneous ensemble deep neural networks

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  • 发表日期：

    2026年1月12日

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  • Slawomir Szrama

摘要

Aircraft turbofan engines undergo gradual performance degradation that, if left undetected, can compromise flight safety and increase maintenance costs. In this study, we propose a heterogeneous ensemble deep neural network (HEDNN) for multi-class health status prediction of real-life turbofan engines. Our methodology begins with the collection of 43,492 real operational records (2012–2024) from a dual-spool, variable geometry turbofan, each run sampled at a fixed throttle setting. We preprocess raw sensor outputs using a robust z-score (median absolute deviation) filter to remove outliers, apply a 35-sample moving median imputation to address 1,812 missing temperature entries, and eliminate any non-numeric artifacts. The cleaned dataset consisting of 97 predictive features after FADEC upgrade harmonization is partitioned into training (80%, n = 34 794) and testing (20%, n = 8 698) sets, and labeled into three health classes (no/low degradation, mid-life degradation, and advanced degradation) built upon performance margin (PMAR) and specific fuel consumption (SMAR) indices.

We engineer three base learners: bidirectional LSTM (128 hidden units), one-dimensional CNN (11 convolution normalization ELU blocks), and BiGRU (four GRU layers, custom flip/concatenate stages), each trained for up to 60 epochs, batch size 16, using the Adam optimizer with piecewise learning rate decay (initial η = 0.01). Individual networks produce class probability vectors that are fused in a shallow fully connected ensemble, finalized by softmax classification.

Performance is assessed via RMSE, MAPE, overall accuracy, precision, recall, F1 score, and multi-class AUC. The HEDNN achieves RMSE = 0.3421 (33–41% lower than any single model), MAPE = 4.78% (60–65% reduction), and accuracy = 95.32%, with F1 = 0.928 and mean AUC = 0.777. In contrast, the best individual learner (BiLSTM) reaches RMSE = 0.5149, MAPE = 12.17%, accuracy = 82.44%, and AUC = 0.717. These results demonstrate that our heterogeneous ensemble markedly outperforms standalone architectures, offering a robust, data-driven prognostic tool for predictive maintenance of turbofan fleets.