Developing machine learning-based models to estimate time to failure for PHM

DOIResolve DOI: http://doi.org/10.1109/ICPHM.2016.7542876
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TypeArticle
Proceedings title2016 IEEE International Conference on Prognostics and Health Management (ICPHM)
Conference2016 IEEE International Conference on Prognostics and Health Management (ICPHM), June 20-22, 2016, Ottawa, ON, Canada
ISBN978-1-5090-0382-2
Pages16
AbstractThe core of PHM (Prognostic and Health Monitoring) technology is prognostics which is able to estimate time to failure (TTF) for the monitored components or systems using the built-in predictive models. However the development of predictive models for TTF estimation remains a challenge. To address this issue, we proposed to develop machine learning-based models for TTF estimation by using the techniques from machine learning and data mining. In the past decade, we have been working on the development of machine learning-based models for estimating TTF and applied the developed technology to various real-world applications such as train wheel prognostics, and aircraft engine prognostics. In this paper, we report two kinds of machine learning-based models for estimating TTF, including multistage classification, on-demand regression. The multistage classification improves the TTF estimation over one stage classification by dividing the time window into more small narrow time windows. A case study, APU prognostics, demonstrates the usefulness of the developed methods. The results from the case study show that the machine learning-based modeling method is an effective and feasible way to develop predictive models to estimate TTF for PHM.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number23000672
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Record identifier73ddf38f-568a-4745-87f4-d7094aeb8ef4
Record created2016-08-22
Record modified2016-08-22
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