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

Abstract

Abstract A method of monitoring and early warning for hydroelectric generating sets based on Hotelling’s T-squared statistics(T2) 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 T2. 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 T2 statistics under normal running state is calculated and taken as a warning threshold. Third, a LSTM model is established to predict future values of T2, 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 T2 prediction and realize the early warning for abnormal status.