Application of Particle Filter to On-board life estimation of LED lights

Ruknudeen, Fazludeen; Asokan, S.

doi:10.1109/jphot.2017.2698409

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…To forecast the lumen maintenance life of LEDs, Fan et al (2015) developed a nonlinear filtering prediction approach based on Sequential Monte Carlo (SMC) and Bayesian dynamic recursive particle filter (PF). Investigating the remaining useful life (RUL) of airport ground lighting (AGL), Ruknudeen and Asokan (2017) employed PF and onboard diagnostics to determine L70. Enayati et al (2021) devised a probability density function (PDF) for LED life prediction using the Monte Carlo algorithm (MC) and Nonlinear Kalman filter (IEKF).…”

Section: Filtering Networkmentioning

confidence: 99%

Overview of high-power LED life prediction algorithms

Sun,

Bai,

Zhang

2024

Front. Sustain. Energy Policy

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Life prediction significantly influences the reliability of LED light sources. While high-power LED light sources theoretically offer a lifespan of up to 100,000 h, irreversible damage to components leads to light failure, substantially reducing their actual lifespan. Consequently, accurate life prediction is pivotal for manufacturers to cut costs and enhance economic efficiency. This necessity aligns with the interests of communities, governments, and consumers. Currently, the most extensively employed prediction methods are based on traditional physical models and data-driven approaches. The focal point of current research lies in realizing model fusion, presenting both a hotspot and a challenge. To elucidate the relationships, advantages, and disadvantages of different algorithms and establish the groundwork for LED life prediction algorithm development, this paper first introduces material properties and the light decay model of high-power LED light sources. Subsequently, it discusses the principles and methods of the physical model concerning light source reliability. The paper also presents a review and comparison of recent domestic and foreign light source life prediction models. Finally, it provides insights into the expected future development trends in life prediction.

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Section: Filtering Networkmentioning

confidence: 99%

Overview of high-power LED life prediction algorithms

Sun,

Bai,

Zhang

2024

Front. Sustain. Energy Policy

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“…However, the PoF methods [178][179][180][181][182][183] require comprehensive knowledge of products in advance (e.g., materials and geometries, thermal, electrical, mechanical, life-cycle conditions, and other processes that lead to failures) that always increases the time and cost in actual applications. On the other hand, the data-driven approaches [119,126,[184][185][186][187][188][189][190][191][192][193][194] need sufficient measurement or experimental data to estimate the health conditions and to predict trend thresholds from failure prognostics, but it is not easy to obtain these data in advance, especially for newly introduced LED lighting products. Thus, the fusion-based PHM is believed to solve these concerns by combining the advanced qualities and features of both the PoF and DD approaches.…”

Section: Fusion Prognostics Approach For Light-emitting Diodesmentioning

confidence: 99%

Reliability of Organic Compounds in Microelectronics and Optoelectronics

Driel

Mehr

2022

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“…The filtering and smoothing methods introduced above will be repeated iteratively twice to get the real degradation of the SV. In this paper, two degradation models are selected to estimate the degradation process of the SVs: the exponential model [29] and the Stochastic process model [30]. This means that real degradation will be defined as the measurement in the degradation model described above.…”

Section: Rul Estimation Of the Sv Based On Apf Techniquementioning

confidence: 99%

A remaining useful life estimation method for solenoid valve based on mmWave radar and auxiliary particle filter technique

Liu

et al. 2021

IEICE Electron. Express

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Solenoid valves(SVs) as a kernel component are widely used in various control systems. Through accurately predicting the remaining useful life (RUL) of the SV, predictable maintenance its can be implemented to reduce system maintenance costs and system reliability improves. This paper proposes a frequency modulated continuous wave (FMCW) based on millimeter-wave radar and the auxiliary particle filter (APF) technique to estimate the RUL of SVs. Firstly, a 77GHz FMCW millimeter-wave radar with a single input and multiple outputs(SIMO) is used to obtain the core displacements of two SVs with the same frequencies. A degradation experiment is designed, and the distortion degree of the core displacement is taken as the degradation indicator to construct a degradation data set. Secondly, the APF technique is used to estimate the degradation state and RUL of the SV. Finally, an experiment platform is built using TI's millimeter-wave radar to verify the method proposed in this paper. The experimental results show that the RUL estimation results are satisfactory.

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Application of Particle Filter to On-board life estimation of LED lights

Cited by 9 publications

References 19 publications

Overview of high-power LED life prediction algorithms

Overview of high-power LED life prediction algorithms

Reliability of Organic Compounds in Microelectronics and Optoelectronics

A remaining useful life estimation method for solenoid valve based on mmWave radar and auxiliary particle filter technique

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