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The application of remaining useful life (RUL) prediction has taken greatimportance in terms of energy optimization, cost-effectiveness, and riskmitigation. The existing RUL prediction algorithms mostly constitute deeplearning frameworks. In this paper, we implement LSTM and GRU models andcompare the obtained results with a proposed genetically trained neuralnetwork. The current models solely depend on Adam and SGD for optimization andlearning. Although the models have worked well with these optimizers, evenlittle uncertainties in prognostics prediction can result in huge losses. Wehope to improve the consistency of the predictions by adding another layer ofoptimization using Genetic Algorithms. The hyper-parameters - learning rate andbatch size are optimized beyond manual capacity. These models and the proposedarchitecture are tested on the NASA Turbofan Jet Engine dataset. The optimizedarchitecture can predict the given hyper-parameters autonomously and providesuperior results.

Genetically Optimized Prediction of Remaining Useful Life