Prediction of turbine vibration amplitude based on long short-term memory network
The main axis vibration value of thermal power unit is non-linear,non-stable,sequen-tially related,and is inseparable from the current historical state.The data extracted by actual thermal power plants often show irregularities.For a long time,the data volume is huge.A long-term memory network(LSTM)that is optimized by the sparrow search algorithm(SSA)is proposed to build a deep learning predictive model,and the vibration amplitude of the spherical spindle of the steam turbine is made of higher accuracy and simulation.Compared with non-time-order neural network models and no optimized timing neural network model prediction performance greatly improved.
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