Developing predictive models for time to failure estimation

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/CSCWD.2016.7565977
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Name affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Proceedings titleIEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2016)
Conference2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), May 4-6, 2016, Nanchang, China
ISBN978-1-5090-1915-1
Pages133–138
SubjectPHM; prognostics; time to failure estimation; classification; regression; on-demand regression
Abstract
The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management (PHM) systems. Taking advantage of advances in sensor technologies, PHM systems enable a predictive maintenance strategy through continuously monitoring the health of complex systems. The core of PHM technology is prognostic which is able to estimate time to failure (TTF) for the monitored components or systems using the built-in predictive models. In this paper, the state of the art of TTF estimation will be first reviewed. After introduction of traditional methods of TTF estimation, we will present the developed approaches for estimating TTF, including classification, regression, on-demand regression, Particle Filtering (PF)-based method, and so on. The main purpose of this paper is to summarize the work on TTF estimation technologies developed in the past decade.
Publication date
PublisherIEEE
LanguageEnglish
Peer reviewedYes
NPARC number23000849
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Record identifierd6121610-48ef-47f7-b5fc-a85029acf0bd
Record created2016-10-19
Record modified2020-03-16
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