Genetically Optimized Prediction of Remaining Useful Life
  The application of remaining useful life (RUL) prediction has taken great
importance in terms of energy optimization, cost-effectiveness, and risk
mitigation. The existing RUL prediction algorithms mostly constitute deep
learning frameworks. In this paper, we implement LSTM and GRU models and
compare the obtained results with a proposed genetically trained neural
network. The current models solely depend on Adam and SGD for optimization and
learning. Although the models have worked well with these optimizers, even
little uncertainties in prognostics prediction can result in huge losses. We
hope to improve the consistency of the predictions by adding another layer of
optimization using Genetic Algorithms. The hyper-parameters - learning rate and
batch size are optimized beyond manual capacity. These models and the proposed
architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized
architecture can predict the given hyper-parameters autonomously and provide
superior results.
