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Steam turbine rotor system is a main part of the power production process. Accurate prediction of the turbine rotor operation state leads to timely detection of the hidden danger and accordingly ensures the efficient power production. The vibration severity reflects the vibration intensity and the working condition as well. Since the accuracy of the normal prediction method is not enough, a new model is proposed in this paper that combines the sequence prediction model with the gated recurrent unit (GRU). According to the obtained results, the accuracy is improved through the proposed model. To verify the effectiveness of the model, simulations are performed on the steam turbine rotor unbalance fault data. The experimental results demonstrate that the proposed approach could be utilized for vibration severity prediction as well as state warning of the steam turbine.

Prediction Model Design for Vibration Severity of Rotating Machine Based on Sequence-to-Sequence Neural Network