基于LSTM循环神经网络的汽轮机转子表面应力预测模型

A Turbine Rotor Surface Stress Prediction Model Based on LSTM Recurrent Neural Network

汽轮机转子启动过程中的寿命管理与评估问题非常关注转子表面应力,而应用有限元计算应力时间代价较大,循环神经网络(recurrent neural network,RNN)可快速准确地计算预测汽轮机启动过程中转子表面应力,大大提高在线评估效率。然而传统RNN神经网络存在梯度消失或爆炸以及无法解决长时依赖的问题,影响其实际应用。针对该问题,建立了基于长短期记忆(longshort-termmemory,LSTM)循环神经网络的汽轮机转子表面应力预测模型。在超参数的多组实验中,发现时间步长为3、dropout比例为0.2、隐藏层单元数为6时预测精度达到最佳,验证了预测模型的合理性。通过预测模型与有限元计算模型的耗时对比,发现预测模型耗时大大缩短,验证了预测模型的高效性。对预测模型的普适性分析表明:预测模型可拓展应用于类似时序序列问题的预测中。

The life management and evaluation during the start-up process of the turbine rotor pays much attention to the rotor surface stress, while the time cost of using finite element method to calculate stress is relatively large. Recurrent neural network can quickly and accurately calculate and predict the rotor surface stress during the start-up process of the turbine rotor which greatly improves the online evaluation efficiency. However, the traditional recurrent neural network(RNN) has the problems of gradient disappearance or explosion and cannot solve the long-term dependence which affects its practical application. To solve this problem, a turbine rotor surface stress prediction model based on LSTM( long short-term memory) recurrent neural network was established. In multiple sets of experiments with hyperparameters, it is found that the prediction accuracy is optimal when the time step is 3, the dropout ratio is 0.2, and the number of hidden layer units is 6, which verifies the rationality of the prediction model. By comparing the time cost of the prediction model with that of the finite element calculation model, it is found that the time cost of the prediction model is greatly shortened, which verifies the high efficiency of the prediction model. The universality analysis of the prediction model shows that the prediction model can be extended and applied to the prediction of similar time series problems.