A Hybrid TCN–BiLSTM–Attention Framework for Turboshaft Engine Performance Prediction
TL;DR
A hybrid model integrating a Temporal Convolutional Network, an attention mechanism, and a Bidirectional Long Short-Term Memory network is proposed for predicting turboshaft engine performance parameters, demonstrating that the model possesses excellent generalization performance and holds significant potential for practical engineering applications.
Abstract
Prediction of turboshaft engine performance parameters is essential for engine health management, however, traditional physics-based models and shallow neural networks struggle to effectively model the complex nonlinear characteristics of time-series data. To overcome these limitations, a hybrid model integrating a Temporal Convolutional Network (TCN), an attention mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network is proposed for predicting turboshaft engine performance parameters. Temporal convolution is employed to extract local temporal features, while the BiLSTM network is used to capture long-term bidirectional dependencies. An attention mechanism is further incorporated to assign greater weights to critical time steps. Simulation results show that the proposed model achieves higher prediction accuracy compared to standalone LSTM and TCN models. When applied to the performance prediction of turboshaft engines under different inlet air temperature conditions, the proposed framework consistently maintained the root mean square error values for training and testing between 0.05 and 0.09 across three key performance parameters, while effectively suppressing measurement noise. These results demonstrate that the model possesses excellent generalization performance and holds significant potential for practical engineering applications.