Title :
Bayesian models for life prediction and fault-mode classification in solid state lamps
Author :
Lall, Pradeep ; Junchao Wei ; Sakalaukus, Peter
Author_Institution :
Dept. of Mech. Eng., Auburn Univ., Auburn, AL, USA
Abstract :
A new method has been developed for assessment of the onset of degradation in solid state luminaires to classify failure mechanisms by using metrics beyond lumen degradation that are currently used for identification of failure. Luminous Flux output, Correlated Color Temperature Data on Philips LED Lamps has been gathered under 85°C/85%RH till lamp failure. The acquired data has been used in conjunction with Bayesian Probabilistic Models to identify luminaires with onset of degradation much prior to failure through identification of decision boundaries between lamps with accrued damage and lamps beyond the failure threshold in the feature space. In addition luminaires with different failure modes have been classified separately from healthy pristine luminaires. It is expected that, the new test technique will allow the development of failure distributions without testing till L70 life for the manifestation of failure.
Keywords :
Bayes methods; LED lamps; failure analysis; fault diagnosis; Bayesian probabilistic model; failure distribution; failure identification mechanism; lumen degradation; philips LED lamp life prediction; solid state lamp fault mode classification; solid state luminaires; Aluminum; Arrays; Current measurement; Lead; Polynomials; Power measurement; Standards;
Conference_Titel :
Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE), 2015 16th International Conference on
Conference_Location :
Budapest
Print_ISBN :
978-1-4799-9949-1
DOI :
10.1109/EuroSimE.2015.7103167