Aerospace Fault Diagnosis Accuracy in Aircraft Engine using One-Dimensional Convolutional Neural Networks with Multi-Head Attention Mechanism
Open Access DepositedAerospace Fault Diagnosis Accuracy in Aircraft Engine using One-Dimensional Convolutional Neural Networks with Multi-Head Attention Mechanism This praxis outlines design and testing one-dimensional convolutional neural network, made up of a multi-head self-attention part (MA1DCNN), to classify multi-label faults in the NASA N-CMAPSS data. Normalization, segmentation, and selective oversampling for class balancing were among data operations utilized. The MA1DCNN combines convolutional blocks with self-attention modules and residual connections to capture both local and long-range dependencies efficiently. Key training strategies included focal loss for minority faults, Cosine Annealing Warm Restarts for learning rates, Gaussian noise for robustness, and hyperparameter optimization via Optuna. Additionally, SHAP analysis provided insights into sensor contributions to predictions On 5,335,270 test samples, MA1DCNN achieved a Macro-F1 of 99.46% and a Macro-Precision of 99.92%. While its classification accuracy of 98.16% was slightly below that of other models evaluated in this study, 1DCNN-BiLSTM with CBAM at 98.23% and XGBoost at 98.25%, it demonstrated the highest Macro-Precision and featured a streamlined design that reduces parameters and latency. Its attention modules enhance fault attribution, making MA1DCNN ideal for real-time predictive maintenance in aerospace settings. Future work will focus on evaluating performance under diverse conditions and refining attention heads for improved fault discrimination.
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Akindoju_gwu_0075A_17473.pdf | 2025-12-12 | Open Access |
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