The application of remaining useful life (RUL) prediction is very important 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.

剩余使用寿命（RUL）预测的应用在能源优化，成本效益和降低风险方面非常重要。现有的RUL预测算法主要构成了深度学习框架。在本文中，我们实现了LSTM和GRU模型，并将获得的结果与提出的经过遗传训练的神经网络进行了比较。当前模型仅依靠ADAM和SGD进行优化和学习。尽管这些模型在这些优化器上运行良好，但预测预测中的不确定性很小，也可能导致巨大的损失。我们希望通过使用遗传算法添加另一层优化来提高预测的一致性。超参数-学习速度和批次大小已优化，超出了人工能力。这些模型和建议的体系结构已在NASA Turbofan Jet Engine数据集上进行了测试。优化的体系结构可以自动预测给定的超参数，并提供出色的结果。