In-situ monitoring and anomaly detection for LED packages using a Mahalanobis distance approach

Owing to the long lifetime and high reliability of light-emitting diode (LED) packages, few or any failures should occur during a short-term or accelerated life test. Therefore, a timeand cost-effective qualification test for accurately predicting the long-term lifetime of an LED package is a critical economic and business requirement for adoption of new LEDs. Previous research usually applied offline photometric measurements to collect the direct performance degradation data of LEDs (e.g. luminous flux and chromaticity coordinates). However, these methods incurred measurement errors and significant testing costs. In this paper, an in-situ monitoring method with sensing the indirect performance data (e.g. lead temperatures, inputdriven current, and forward voltage) is proposed to detect the health of LEDs. In this proposed method, a data-driven method using a Mahalanobis distance (MD) approach is employed to detect early anomalies of LEDs before failures happen and transformed MD values are defined as a real-time health indicator to reflect the LED's degradation. The experimental results show that the proposed MD-based anomaly detection approach can provide an early anomaly warning at around 45% of lifetime before actual failure happens for all test LEDs evaluated.

[1]  Xiaobing Luo,et al.  LED Packaging for Lighting Applications: Design, Manufacturing and Testing , 2011 .

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

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

[4]  Michael G. Pecht,et al.  A health indicator method for degradation detection of electronic products , 2012, Microelectron. Reliab..

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

[6]  Stoyan Stoyanov,et al.  Prognostics and Health Monitoring of High Power LED , 2012, Micromachines.

[7]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[8]  Sheng-Tsaing Tseng,et al.  Stochastic Diffusion Modeling of Degradation Data , 2007, Journal of Data Science.

[9]  Haitao Liao,et al.  Reliability inference for field conditions from accelerated degradation testing , 2006 .

[10]  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.

[11]  M. Pecht,et al.  Challenges in the Qualification of Electronic Components and Systems , 2013, IEEE Transactions on Device and Materials Reliability.

[12]  P. V. Varde,et al.  Light emitting diodes reliability review , 2012, Microelectron. Reliab..