Fatigue life prediction of a supercritical steam turbine rotor based on neural networks

Abstract 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.