Parallel prediction method for damage factors and life loss of turbine high-pressure rotors based on soft parameter sharing mechanism of multi-task learning

In order to ensure the safe operation of steam turbines and extend rotor life, this study aims to develop a high-precision rolling prediction method for turbine rotor temperature, thermal stress and life loss at dangerous points. To address the limitations of traditional prediction methods, a parallel prediction method based on the soft parameter sharing of multi-task learning (MTL-SPS-PPM) is proposed. The model employs a soft parameter sharing mechanism to enhance knowledge transfer across tasks, a multi-model fusion strategy to improve feature extraction, and a rolling multi-step prediction framework for long-term trend forecasting. Furthermore, future power scheduling commands are incorporated as input features to improve adaptability to dynamic operational conditions. Experimental results demonstrate that MTL-SPS-PPM significantly outperforms traditional single-task learning models. Compared to the best-performing baseline models, MSE decreased by 0.305, 0.01, and 0.112, MAE decreased by 0.176, 0.135, and 0.089, and R2 increased by 0.14, 0.067, and 0.051 for temperature, thermal stress, and life loss predictions, respectively. The proposed approach achieves higher prediction accuracy and efficiency, providing a reliable tool for real-time health monitoring and operational optimization of turbine units. This work lays the foundation for integrating physics-based modeling with deep learning to enhance predictive performance under complex operating conditions.