A comparison of state-of-the-art machine learning algorithms on fault indication and remaining useful life determination by telemetry data

Date
2021-11-15
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
International Conference on Future Internet of Things and Cloud (FiCloud)
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
79 - 85
Language
English
Journal Title
Journal ISSN
Volume Title
Series
Abstract

Contemporary trends in the diffusion of artificial intelligence technologies has increased the number of studies on predictive maintenance, a recent focus of interest in many industrial domains. Despite the increased interest in the use of machine learning for predictive maintenance, few studies involve thorough comparisons of machine learning algorithms' performance on predictive maintenance applications. This work aims to predict the remaining useful life and machine failures and compares five different algorithms: Random Forest, Gradient Boosted Tree, K-Nearest Neighbors, Multilayer Perceptron and LightGBM. Our results suggest better performances for binary classification using Random Forest, and for regression using LightGRM comnared to other selected algorithms.

Course
Other identifiers
Book Title
Citation
Published Version (Please cite this version)