A Comparison of Prediction Models with Machine Learning Algorithms for Traction Characteristics in Linear Traction Induction Motors

Zeng, Dihui; Ge, Qiongxuan; Degano, Michele

Abstract

This paper compares the machine learning algorithm‐based prediction methods for the traction characteristics in linear traction induction motors operating at common working conditions, i.e. different slip and running velocity, with a symmetric or asymmetric secondary. These models provide a method for obtaining the dynamic characteristics in the motor that considers nonlinear effects. First, some analytical results for the prototype machine under different working conditions is calculated. Second, classification and feature extraction of traction characteristics results including thrust, transversal and vertical forces is made according to the different slip, running speed and lateral secondary displacement, and the results set is divided into training sets and test sets. Third, the prediction model established by different machine learning algorithms are analyzed and compared in principle. These algorithms in this paper mainly contain: artificial neural networks (ANNs), linear regression (LR), symbolic regression using GP, k‐Nearest Neighbour (kNN), random forests (RFRs). The machine learning algorithm‐based prediction methods are trained with the training set, and then the verified with the test set. Finally, this paper discusses the most optimal model for predicting traction characteristics. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Citation

Zeng, D., Ge, Q., & Degano, M. (2022). A Comparison of Prediction Models with Machine Learning Algorithms for Traction Characteristics in Linear Traction Induction Motors. IEEJ Transactions on Electrical and Electronic Engineering, 17(3), 470-478. https://doi.org/10.1002/tee.23534

• Journal Article Type: Article
• Acceptance Date: Aug 26, 2021
• Online Publication Date: Dec 19, 2021
• Publication Date: Mar 1, 2022
• Deposit Date: Nov 26, 2024
• Journal: IEEJ Transactions on Electrical and Electronic Engineering
• Print ISSN: 1931-4973
• Electronic ISSN: 1931-4981
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 3
• Pages: 470-478
• DOI: https://doi.org/10.1002/tee.23534
• Keywords: Electrical and Electronic Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/25344186
• Publisher URL: https://onlinelibrary.wiley.com/doi/10.1002/tee.23534