Prognosis of Blade Material Fatigue Using Elman Neural Networks
Prognosis of major components such as blades, rotors, valves of steam turbine is crucial to reducing operating and maintenance costs. Prognostic strategies can assist to detect, classify and predict developing faults, guarantee reliable, efficient and continuous operation of electric plants, and may even result in saving lives. In this paper, a recurrent neural network based strategy was developed for blade material degradation assessment and fatigue damage propagation prediction. Two Elman Neural Networks were developed for fatigue severity assessment and trend prediction correspondingly. The performance of the proposed prognostic methodology was evaluated by using blade material fatigue data collected from a material testing system. The prognostic method is found to be a reliable and robust material fatigue predictor.
Kai Cheng, Yingxue Yao and Liang Zhou
J. H. Yan and P.X. Wang, "Prognosis of Blade Material Fatigue Using Elman Neural Networks", Applied Mechanics and Materials, Vols. 10-12, pp. 558-562, 2008