Genetically optimized prediction of remaining useful life

• Published in: Sustainable computing informatics and systems 2021-09, Vol.31, p.100565, Article 100565
• Main Authors: Agrawal, Shaashwat, Sarkar, Sagnik, Srivastava, Gautam, Reddy Maddikunta, Praveen Kumar, Gadekallu, Thippa Reddy
• Format: Article
• Language: English
• Subjects: Batch size; Genetically trained neural network; GRU; Hyper-parameters; Learning rate; LSTM; Prognostic; Remaining useful life
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2021.100565

•The NASA dataset is trained with a tuned 2-layer LSTM and GRU models.•The results are compared to existing outputs and inference shown.•Introduce a semi-novel optimization algorithm using genetic algorithms.•Hyper-parameters of the model – learning rate and batch size are self-tuned by a genetic algorithm. 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.