Remaining Useful Life Prediction for Experimental Filtration System: A Data Challenge
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Maintenance costs of industrial systems often exceed the initial investment cost. Predictive maintenance, which analyzes the health of the system and suggests maintenance planning, is one of the strategies implemented to reduce maintenance costs. Health status and life estimation of the machinery are the most researched topics in this context. In this paper, we present our analysis for Fifth European Conference of the Prognostics and Health Management Society 2020 Data Challenge, which introduces an experimental filtration system for different experiment setups, and asks for remaining useful life predictions. We compared random forest, gradient boosting, and Gaussian process regression algorithms to predict the useful life of the experimental system. With the help of a new fault-based piecewise linear RUL assignment strategy, our gradient boosting based solution has been ranked 3rd in the data challenge.
How to Cite
##plugins.themes.bootstrap3.article.details##
data challenge
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.