Prognostics of lumen maintenance for High power white light emitting diodes using a nonlinear filter-based approach

High power white light emitting diodes (HPWLEDs), with advantages in terms of luminous efficacy, energy saving, and reliability, have become a popular alternative to conventional luminaires as white light sources. Like other new electronic products, HPWLEDs must also undergo qualification testing before being released to the market. However, most traditional qualification tests, which require all devices under testing to fail, are time-consuming and expensive. Nowadays, as recommended by the Illuminating Engineering Society (IES, IES-TM-21-11), many LED manufacturers use a projecting approach based on short-term collected light output data to predict the future lumen maintenance (or lumen lifetime) of LEDs. However, this projecting approach, which depends on the least-square regression method, generates large prediction errors and uncertainties in real applications. To improve the prediction accuracy, we present in this paper a nonlinear filter-based prognostic approach (the recursive Unscented Kalman Filter) to predict the lumen maintenance of HPWLEDs based on the short-term observed data. The prognostic performance of the proposed approach and the IES-TM-21-11 projecting approach are compared and evaluated with both accuracy- and precision-based metrics.

[1]  Woosoon Yim,et al.  Proceedings of the 1995 American Control Conference , 1995 .

[2]  Jong Kyu Kim,et al.  Solid-State Light Sources Getting Smart , 2005, Science.

[3]  Ron Lenk,et al.  Practical Lighting Design with LEDs , 2011 .

[4]  Pradeep Lall,et al.  Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics , 2012, IEEE Transactions on Industrial Electronics.

[5]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[6]  Michael Pecht,et al.  Prognostics of Chromaticity State for Phosphor-Converted White Light Emitting Diodes Using an Unscented Kalman Filter Approach , 2014, IEEE Transactions on Device and Materials Reliability.

[7]  Yuanxin Wu,et al.  Unscented Kalman filtering for additive noise case: augmented vs. non-augmented , 2005, Proceedings of the 2005, American Control Conference, 2005..

[8]  Jean Paul Freyssinier,et al.  Solid-state lighting: failure analysis of white LEDs , 2004 .

[9]  E. Schubert,et al.  Analysis of high-power packages for phosphor-based white-light-emitting diodes , 2005 .

[10]  D. H. Mash,et al.  Light-emitting diodes , 1977, Nature.

[11]  Cristian Lascu,et al.  State Estimation of Induction Motor Drives Using the Unscented Kalman Filter , 2012, IEEE Transactions on Industrial Electronics.

[12]  S. K. Yang,et al.  State estimation for predictive maintenance using Kalman filter , 1999 .

[13]  A. Saltelli,et al.  Reliability Engineering and System Safety , 2008 .

[14]  M. Pecht,et al.  Lifetime Estimation of High-Power White LED Using Degradation-Data-Driven Method , 2012, IEEE Transactions on Device and Materials Reliability.

[15]  S. K. Yang,et al.  An experiment of state estimation for predictive maintenance using Kalman filter on a DC motor , 2002, Reliab. Eng. Syst. Saf..

[16]  Patrick Mottier,et al.  LEDs for lighting applications , 2009 .

[17]  M. Pecht,et al.  Physics-of-Failure-Based Prognostics and Health Management for High-Power White Light-Emitting Diode Lighting , 2011, IEEE Transactions on Device and Materials Reliability.

[18]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[19]  Enrico Zio,et al.  Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..

[20]  Yuanxin Wu,et al.  Unscented Kalman filtering for additive noise case: augmented versus nonaugmented , 2005, IEEE Signal Processing Letters.

[21]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[22]  Kai Wang,et al.  Status and prospects for phosphor-based white LED packaging , 2009 .

[23]  B. Saha,et al.  A comparison of filter-based approaches for model-based prognostics , 2012, 2012 IEEE Aerospace Conference.

[24]  H. Sorenson.  Least-squares estimation: from Gauss to Kalman , 1970, IEEE Spectrum.

[25]  Shinya Ishizaki,et al.  Lifetime Estimation of High Power White LEDs , 2007 .

[26]  Shriram Santhanagopalan,et al.  State of charge estimation using an unscented filter for high power lithium ion cells , 2010 .

[27]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[28]  P. Lall,et al.  Prognostics and health management of electronics , 2006, 2006 11th International Symposium on Advanced Packaging Materials: Processes, Properties and Interface.

[29]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[30]  Y. Ma,et al.  Phosphors in phosphor-converted white light-emitting diodes: Recent advances in materials, techniques and properties , 2010 .

[31]  Bruce P. Gibbs,et al.  Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook , 2011 .

[32]  Jinwhan Kim,et al.  Comparison Between Nonlinear Filtering Techniques for Spiraling Ballistic Missile State Estimation , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[33]  Congkao Wen,et al.  Systematics of one-loop scattering amplitudes in N=4 super-Yang–Mills theories , 2005 .

[34]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.