Monitoring and Early Warning for Hydroelectric Generating Sets based on Hotelling’s T2 and LSTM Model

Abstract A method of monitoring and early warning for hydroelectric generating sets based on Hotelling’s T-squared statistics(T 2 ) and Long Short-Term Memory(LSTM) network is proposed. Multi-channel vibration and swing signals can be fused and predicted based on the given model. The monitoring and alerting function can also be implemented according to a threshold value of T 2 . First, the vibration and swing signals of multi-channels hydroelectric generating sets are obtained and fused based on Principal Component Analysis(PCA) to reduce the amount of data. Second, Hotelling’s T 2 statistics under normal running state is calculated and taken as a warning threshold. Third, a LSTM model is established to predict future values of T 2 , and early warning for a hydroelectric generating set can be realized by use of the obtained warning threshold. The vibration and swing signals from 16 channels in a set are used to validate the effectiveness of the method. Finally, there is a more than 90% reduction in the amount of data and the efficiency is significantly improved. LSTM has a high accuracy in T 2 prediction and realize the early warning for abnormal status.