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 compar...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Agrawal, Shaashwat, Sarkar, Sagnik, Srivastava, Gautam, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu
Format: Article
Language:English
Subjects:
Algorithms
Genetic algorithms
Jet engines
Machine learning
Mathematical models
Neural networks
Optimization
Parameters
Turbofans
Useful life
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2102.08845