{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:16:10Z","timestamp":1765887370544,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2012,2,24]],"date-time":"2012-02-24T00:00:00Z","timestamp":1330041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>Prognostics is seen as a key component of health usage monitoring systems, where prognostics algorithms can both detect anomalies in the behavior\/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 incandescent 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.<\/jats:p>","DOI":"10.3390\/mi3010078","type":"journal-article","created":{"date-parts":[[2012,2,24]],"date-time":"2012-02-24T11:37:07Z","timestamp":1330083427000},"page":"78-100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Prognostics and Health Monitoring of High Power LED"],"prefix":"10.3390","volume":"3","author":[{"given":"Thamo","family":"Sutharssan","sequence":"first","affiliation":[{"name":"Computational Mechanics and Reliability Group, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stoyan","family":"Stoyanov","sequence":"additional","affiliation":[{"name":"Computational Mechanics and Reliability Group, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Bailey","sequence":"additional","affiliation":[{"name":"Computational Mechanics and Reliability Group, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasmine","family":"Rosunally","sequence":"additional","affiliation":[{"name":"Computational Mechanics and Reliability Group, University of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2012,2,24]]},"reference":[{"key":"ref_1","unstructured":"Narendran, N., Gu, Y., Jayasinghe, L., Freyssinier, J.P., and Zhu, Y. 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