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A review of data-driven prognostics in power electronics

A review of data-driven prognostics in power electronics

Kabir, Ahsanul, Bailey, Christopher ORCID: 0000-0002-9438-3879, Lu, Hua ORCID: 0000-0002-4392-6562 and Stoyanov, Stoyan ORCID: 0000-0001-6091-1226 (2012) A review of data-driven prognostics in power electronics. In: 2012 35th International Spring Seminar on Electronics Technology. IEEE Conference Publications . Institute of Electrical and Electronic Engineers, Inc., Piscataway, N.J., USA, pp. 189-192. ISBN 978-1-4673-2241-6 (Print), 978-1-4673-2239-3 (Online) ISSN 2161-2528 (doi:https://doi.org/10.1109/ISSE.2012.6273136)

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Abstract

The discipline that connects prognostics and system lifecycle management is often referred to as prognostics and health management (PHM). Though prognostics is one of the main parts of PHM, it is the least mature. This paper is based on the past work of data driven prognostics applied in the field of power electronics modules and primarily concerned with the data driven prognostics methods that take advantage of measured characteristics of individual systems or components in order to predict the remaining useful life (RUL).

Item Type: Conference Proceedings
Title of Proceedings: 2012 35th International Spring Seminar on Electronics Technology
Additional Information: [1] This paper was presented at the 2012 35th International Spring Seminar (ISSE) on Electronics Technology held from 9-13 May 2012 in Bad Aussee, Austria. [2] INSPEC Accession Number: 12945491. [3] This work is supported by the Computational Mechanics & Reliability Group (“CMRG”, http://cmrg.gre.ac.uk/) at the University of Greenwich.
Uncontrolled Keywords: power electronics, data-driven prognostics, insulated gate bipolar transistors, mathematical model, monitoring, physics, predictive models, prognostics and health management
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 20 Mar 2019 11:54
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/9448

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