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

Ibrahim, Mesfin Seid; Fan, Jiajie; Yung, Winco K.C.; Prisacaru, Alexandru; Driel, W.D. van; Fan, Xuejun; Zhang, Guoqi

doi:10.1002/lpor.202000254

Cited by 55 publications

(22 citation statements)

References 170 publications

(292 reference statements)

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“…In fact, the strength of machine learning lies in the analysis of large amounts of experimental data which are often routinely collected in the industrial LED production. The combination of reality-trained artificial neural networks (ANNs) with numerical simulations could lead to the creation of realistic digital twins that support the LED design and production process [ 118 , 131 , 132 ].…”

Section: Key Modeling and Simulation Challengesmentioning

confidence: 99%

Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges

Piprek¹

2020

Materials

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Light-emitting diodes (LEDs) based on Gallium Nitride (GaN) have been revolutionizing various applications in lighting, displays, biotechnology, and other fields. However, their energy efficiency is still below expectations in many cases. An unprecedented diversity of theoretical models has been developed for efficiency analysis and GaN-LED design optimization, including carrier transport models, quantum well recombination models, and light extraction models. This invited review paper provides an overview of the modeling landscape and pays special attention to the influence of III-nitride material properties. It thereby identifies some key challenges and directions for future improvements.

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Section: Key Modeling and Simulation Challengesmentioning

confidence: 99%

Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges

Piprek¹

2020

Materials

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“…Tao et al [22][23][24] provided three reviews that depict digital twin applications and comparisons of digital twins and big data in industry. Minerva et al [25] analyzed the advanced technologies used in manufacturing industry since the advent of the digital twin, which could inspire researchers in other fields. Ibrahim et al [26] systematically summarized the effectiveness of machine learning algorithms in lightemitting diodes fault diagnosis and prognostics and presented the challenges and prospects of the digital twin in this field.…”

Section: Introductionmentioning

confidence: 99%

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

Yang¹,

Karimi²,

Kaynak³

et al. 2021

CES

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Digitization and digitalization have already changed our world significantly. Further disruptions are imminent with the ongoing digital transformation, a major component of which is digital twins. As the big data techniques, Internet of Things, cloud computing, and artificial intelligence algorithms advance, the digital twin technology has entered a phase of rapid development. It has been stated to be one of the top ten most promising technologies. Although it is still in its early stages, digital twins are already being widely used in a variety of fields, especially in industry, smart cities, and smart health, which are points that attract most researchers to study. In the literature, there can be seen numerous articles and reviews on digital twins, published every year in these three fields. It is therefore timely, even necessary, to provide an analysis of the published work. This is the motivation behind this article, the focus of which is the major research and application areas of digital twins. The survey first analyzes the recent developments of digital twins, then summarizes the theoretical underpinnings of the technology, and finally concludes with specific developments in various application areas of digital twins. It also discusses the challenges that may be encountered in the future.

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“…In this study, we make use of inputs from FTA results and expert knowledge for LED structural and functional analysis. Despite the shortcoming of traditional approaches, Bayesian Network (BN) is found to be a suitable method for complex system reliability analysis [30][31][32], due to its advantages in handling uncertainties, correlations, and the conditional relationship between components/subsystems [31]. As one of the popular modelling and reasoning tools, the BN model has been employed in the fields of machine learning, artificial intelligence, and uncertainty management [33].…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 55 publications

References 170 publications

Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges

Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges

Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey

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

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