Work

A Hybrid TCN–BiLSTM–Attention Framework for Turboshaft Engine Performance Prediction

Year: 2026

Type: article

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.

Source: Aerospace

DOI: 10.3390/aerospace13090810

Authors: Chengjiu Wang, Jingru Chen, Xin Zhou, Jinquan Huang, Feng Lu

Institutions: Aviation Industry Corporation of China (China), Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Aerospace Engineering and Applications

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896