Genetically Optimized Prediction of Remaining Useful Life
This work addresses the need for more reliable RUL predictions in industrial maintenance to prevent losses, but it is incremental as it builds on existing deep learning methods by adding genetic algorithm optimization.
The paper tackles the problem of improving consistency in remaining useful life (RUL) prediction for turbofan jet engines by proposing a genetically trained neural network that optimizes hyper-parameters like learning rate and batch size, achieving superior results compared to LSTM and GRU models on the NASA dataset.
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.