Bayesian Networks

González-Brenes, José P.; Behrens, John T.; Mislevy, Robert J.; Levy, Roy; DiCerbo, Kristen E.

doi:10.1002/9781118956588.ch14

Cited by 5 publications

References 28 publications

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Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes

Ibrahim

Fan

Yung

et al. 2020

Laser & Photonics Reviews

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Light-emitting diodes (LEDs) are among the key innovations that have revolutionized the lighting industry, due to their versatility in applications, higher reliability, longer lifetime, and higher efficiency compared with other light sources. The demand for increased lifetime and higher reliability has attracted a significant number of research studies on the prognostics and lifetime estimation of LEDs, ranging from the traditional failure data analysis to the latest degradation modeling and machine learning based approaches over the past couple of years. However, there is a lack of reviews that systematically address the currently evolving machine learning algorithms and methods for fault detection, diagnostics, and lifetime prediction of LEDs. To address those deficiencies, a review on the diagnostic and prognostic methods and algorithms based on machine learning that helps to improve system performance, reliability, and lifetime assessment of LEDs is provided. The fundamental principles, pros and cons of methods including artificial neural networks, principal component analysis, hidden Markov models, support vector machines, and Bayesian networks are presented. Finally, discussion on the prospects of the machine learning implementation from LED packages, components to system level reliability analysis, potential challenges and opportunities, and the future digital twin technology for LEDs lifetime analysis is provided.

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Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes

Ibrahim

Fan

Yung

et al. 2020

Laser & Photonics Reviews

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Health Monitoring, Machine Learning, and Digital Twin for LED Degradation Analysis

Ibrahim

Zhou

Fan

2022

Reliability of Organic Compounds in Microelectronics and Optoelectronics

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

et al. 2021

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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.

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Bayesian Networks

Cited by 5 publications

References 28 publications

Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes

Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes

Health Monitoring, Machine Learning, and Digital Twin for LED Degradation Analysis

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

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