The use of artificial neural network (ANN) for modeling the useful life of the failure assessment in blades of steam turbines

• The useful life of steam turbine blades are analyzed with artificial neural networks
• Experimental and simulated data were compared satisfactorily with R2 = 0.9913
• The proposed model passed the test with 99% confidence level.
• The resonance stress has relative importance of 35.5%. in terms of useful life.

Steam turbines have many applications in various industrial sectors and by common experience blade failures are the main origin of operational breakdowns in these machines, causing great economic loss in turbo machinery industry. The turbines are designed to work in stable conditions of operation. Nevertheless, failure in blades has been present after a short time period of work. These failures commonly attributed to resonance stress of the blades at different stages to certain excitation frequencies. Artificial neural network (ANN) approach was developed to predict the useful life (UL) of the blades. The configuration 6–3–1 (6 inputs, 3 hidden and 1 output neurons) presented an excellent agreement (R2 = 0.9912 and RMSE = 0.00022) between experimental and simulated useful life value considering the hyperbolic tangent sigmoid and linear transfer function in the hidden layer and output layer. In the following study, the sensitivity analysis was carried out, and showed, also that all studied input variables (resonance stress, frequency ratio, dynamic stress, damping, fatigue strength, mean stress) have strong effect on blades steam turbines in terms of useful life. However, the resonance stress is the most influential parameter with relative importance of 35.5%, followed by frequency ratio.The results showed that neural network modeling could effectively predict and simulate the behavior of life cycles assessment in blades of steam turbines.