System level reliability assessment for high power light-emitting diode lamp based on a Bayesian network method

System level reliability assessment for high power light-emitting diode lamp based on a Bayesian network method

Ibrahim, Mesfin Seid (The Hong Kong Polytechnic University; Wollo University)
Fan, J. (TU Delft Electronic Components, Technology and Materials; Fudan University; Changzhou Institute of Technology Research for Solid State Lighting)
Yung, Winco K.C. (The Hong Kong Polytechnic University)
Jing, Zhou (Hohai University)
Fan, Xuejun (Lamar University)
van Driel, W.D. (TU Delft Electronic Components, Technology and Materials; Signify)
Zhang, Kouchi (TU Delft Electronic Components, Technology and Materials)

2021

The increased system complexity in electronic products brings challenges in a system level reliability assessment and lifetime estimation. Traditionally, the graph model-based reliability block diagrams (RBD) and fault tree analysis (FTA) have been used to assess the reliability of products and systems. However, these methods are based on deterministic relationships between components that introduce prediction inaccuracy. To fill the gap, a Bayesian Network (BN) method is introduced that considers the intricacies of the high-power light-emitting diode (LED) lamp system and the functional interaction among components for reliability assessment and lifetime prediction. An accelerated degradation test was conducted to analyze the evolution of the degradation and failure of components that influence the system level lifetime and performance of LED lamps. The Gamma process and Weibull distribution are used for component level lifetime prediction. The junction tree algorithm was deployed in the BN structure to estimate the joint probability distributions of the lifetime states. The degradation and prediction results showed that LED modules contribute a major part for lumen degradation of LED lamps followed by drivers and the least effect is from diffuser and reflector. The BN based lifetime estimation results also exhibited an accurate prediction as validated with the Gamma process and such improved reliability assessment outcomes are beneficial to LED manufacturers and customers. Thus, the proposed approach is effective to evaluate and address the long-term reliability assessment concerns of high-reliability LED lamps and fulfill the guarantee of high prediction accuracy in less time and cost-effective manner.

Bayesian networks (BN)
Junction tree algorithm (JTA)
Light-emitting diodes (LEDs)
Reliability assessment
System level lifetime prediction

http://resolver.tudelft.nl/uuid:a5d25e33-317d-47b4-aa8f-d2e3c9253112

https://doi.org/10.1016/j.measurement.2021.109191

2021-09-01

0263-2241

Measurement, 176, 1-13

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2021 Mesfin Seid Ibrahim, J. Fan, Winco K.C. Yung, Zhou Jing, Xuejun Fan, W.D. van Driel, Kouchi Zhang