Development and Engineering Application of Fault Early Warning Model for Large Hydropower Turbine Based on Principal Component Analysis and Long Short-Term Memory Network
Youchun Pi, Yanmu Chen, Shengbo Wang, Yun Tan, Xiaomo Jiang, Xiaofang Wang, Lunjun Ding, Linzhi Zhang, Yeming Lu · IEEE Sensors Journal · 2025
With the expansion of hydropower stations and the complexity of operational environments, ensuring the safety of large hydro-turbine units has become critical for hydropower station management. Current studies reveal challenges in intelligent monitoring and fault warning due to complex conditions and massive data, necessitating innovative methods for multi-dimensional data processing, risk identification, and accurate fault prediction. This study addresses the challenge of real-time operational data processing to enhance fault early warning accuracy for large hydro-turbine units. Data is classified by monitoring locations and signal types, followed by governance using outlier analysis, KNN-based missing value imputation, and Bayesian-wavelet packet denoising. A PCA-LSTM fault early warning model is proposed, where PCA reduces dimensionality and extracts representative features, and LSTM captures time-series dependencies for fault prediction. Validation shows R² metrics of 0.969 – 0.996 and MAE of 0.048 – 0.502 for PCA1 – PCA6. Application to historical faults in A Hydropower Station demonstrates early warning times of 127–304 time steps and failure warning times of 33–170 time steps ahead of actual failures, highlighting the model’s timeliness.