Disk Drives Remaining Useful Life Prediction Using the Extreme Learning Machine
Abstract
The article deals with the problem of predicting the remaining useful life of disk drives using a machine learning model, in particular, using an Extreme Learning Machine (ELM). A method is proposed for improving the values of model quality metrics by generating new features, as well as their selection using a method that implements the calculation of the symmetric Kullback-Leibler Divergence (SKLD). It is shown that a model based on an extreme learning machine and trained on the basis of a dataset formed from the results of generation of new features and their subsequent selection by the SKL method can predict the remaining useful life with an average error of 2.5 days, while model training time is about 6 seconds. The results of a comparative analysis are presented, confirming the efficiency of the proposed model based on ELM. Additionally, the methods for generating features BY and MI are compared, and their shortcomings over SKLD in this case are demonstrated.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Demidova, L. A., & Fursov, I. A. (2023). Disk Drives Remaining Useful Life Prediction Using the Extreme Learning Machine. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 304-312). European Publisher. https://doi.org/10.15405/epct.23021.37