Sutharssan, Thamo, Stoyanov, Stoyan ORCID: 0000-0001-6091-1226, Bailey, Christopher ORCID: 0000-0002-9438-3879 and Rosunally, Yasmine (2012) Prognostics and health monitoring of high power LED. Micromachines, 3 (1). pp. 78-100. ISSN 2072-666X (doi:https://doi.org/10.3390/mi3010078)

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Abstract

Prognostics is seen as a key component of health usage monitoring systems, where prognostics algorithms can both detect anomalies in the behaviour/performance of a micro-device/system, and predict its remaining useful life when subjected to monitored operational and environmental conditions. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incadescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. For some LED applications there is a requirement to monitor the health of LED lighting systems and predict when failure is likely to occur. This is very important in the case of safety critical and emergency applications. This paper provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions.

Item Type:	Article
Additional Information:	[1] First published: 24 February 2012. [2] Published as: Micromachines, (2012), 3, (1) pp. 78-100. [3] This article belongs to the Special Issue Health and Usage Monitoring Microsystems (HUMMs). [4] Micromachines is an online only, open access journal. [5] This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/)[3] Citation: Sutharssan, T.; Stoyanov, S.; Bailey, C.; Rosunally, Y. Prognostics and Health Monitoring of High Power LED. Micromachines 2012, 3, 78-100.
Uncontrolled Keywords:	data driven prognostics, health monitoring, light emitting diodes, LEDs
Subjects:	Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery
Pre-2014 Departments:	School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Mechanics & Reliability Group School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:	Publisher Organisation Organisation
Last Modified:	13 Mar 2019 11:34
URI:	http://gala.gre.ac.uk/id/eprint/7714

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