The application of remaining useful life (RUL) prediction is very important in terms energy optimization, cost-effectiveness, and risk mitigation. existing RUL algorithms mostly constitute deep learning frameworks. In this paper, we implement LSTM GRU models compare the obtained results with a proposed genetically trained neural network. current solely depend on ADAM SGD for optimization learni...

Fatigue crack growth is a random process and various uncertainties affect the remaining useful life of engineering materials and structures. A general probabilistic life prediction methodology for accurate and efficient fatigue prognosis is proposed in this paper. The proposed methodology is based-on an inverse first-order reliability method (FORM) to evaluate the fatigue life at an arbitrary r...