The safety and stability of rotors are significantly important for smooth operations of steam turbines. To predict the fatigue life of a 350 MW supercritical steam turbine rotor online, a data-driven based neural network is proposed in this paper. Finite element analysis is employed to determine the danger zones of the whole rotor and then a large sample dataset consisted of temperatures and stresses is established for subsequent neural network training. Different from the traditional thermo-elasto-plastic or finite element methods, the proposed approach can effectively calculate temperatures and stresses at the danger zones by inputting measured parameters. The Neuber rule and Manson-Coffin equation are used to estimate the fatigue life of the rotor. It is shown that the proposed neural network-based method can assess the operating status of steam turbine during different cold startups and provide a feasible online health monitoring methodology for steam turbine rotor, without dealing with the quite challenging thermo-mechanical analysis.

转子的安全性和稳定性对于汽轮机的平稳运行非常重要。为了在线预测 350 MW 超临界汽轮机转子的疲劳寿命，本文提出了一种基于数据驱动的神经网络。采用有限元分析确定整个转子的危险区域，然后建立由温度和应力组成的大样本数据集，用于后续的神经网络训练。与传统的热弹塑性或有限元方法不同，该方法可以通过输入测量参数有效地计算危险区域的温度和应力。Neuber 规则和 Manson-Coffin 方程用于估计转子的疲劳寿命。