Lifetime prediction of a multi-chip high-power LED light source based on artificial neural networks

Liu, Hongwei; Yu, Dawei; Niu, Pingjuan; Zhang, Zanyun; Guo, Kai; Wang, Di; Zhang, Jianxin; Ma, Xin; Jia, Chengkui; Wu, Chaoyu

doi:10.1016/j.rinp.2018.11.001

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…The requested lifetime L is generally not measured. The Illumination Engineers Society (IES) [38] assigns the measurement methods as per the industry norm IES LM-80 [27]. It requires that the LED lamps are tested for at least 6000 hours with a sufficient number of samples and data.…”

Section: Resultsmentioning

confidence: 99%

“…After that, the lifetime was estimated according to test report in the IES LM-80 with the exponential light exchange model as described by the standard IES TM-21 [32]. In this study, for the modeling of the ANFIS system, the light output maintenance values of the COB LED fixtures [27,32]. Table 4 presents summary information on the current, temperature, luminous flux, and lifetime values of the tested LED fixtures.…”

Section: Resultsmentioning

confidence: 99%

“…Park and Kim used the gamma model to predict the service life of LEDs [5], and Zhang et al estimated the mean time to failure (MTTF) using exponential distribution [26]. Liu et al studied the ANN distribution for temperature and lifetime in multichip LEDs [27], and Alfarog et al used the thermally connected FEM [28]. Niu et al studied the effects of LED driver Al-Cap core on life parameters [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Kıyak

Gökmen

Koçyiğit

2021

Journal of Nanomaterials

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Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Kıyak

Gökmen

Koçyiğit

2021

Journal of Nanomaterials

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“…The results showed that using a simplified PET model, temperature distribution analysis was performed using the finite element approach with a temperature inaccuracy of ±0.3 K. According to the findings, the ANN simplified PET model can be utilized as a reference tool for designing the best thermal dissipation structure for LED lamps. Liu et al 28 demonstrated that ANN can predict the lifetime of a multi-chip high-power LED light source based on the precise LED temperature distribution. Finite element method with LED chip PET ANN is used to calculate the temperature distribution of the high power light source.…”

Section: Introductionmentioning

confidence: 99%

Comparison and validation of neural network models to estimate LED spectral power distribution

Lokesh

Padmasali

Mahesha

et al. 2023

Lighting Research & Technology

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The spectral power distribution (SPD) is the true fingerprint of a light source and is mainly dependent on electrical and thermal loading. Both the photometric and colorimetric quantities are originally extracted from SPD. Therefore, the dynamic prediction of SPD for LED has become an important aspect to evaluate the performance of LED during its time of operation. Generally, the statistical models are used to predict SPD. However, the statistical model with more than two input makes the system complex and time demanding. Artificial Neural Network (ANN) models, on the other hand, can help with this problem. The major goal of this research is to improve the utility of ANN in lighting applications. This is demonstrated by various neural network (NN) structures referred as models 1, 2 and 3 with combinations of varied neurons and hidden layers (HLs) to forecast SPD for various electrical and thermal stress levels at zero hours. The results are compared and based on absolute prediction error (APE) set to 5%, model 1 is considered as the best model for the SPD prediction. In addition, the time-based SPD prediction with model 1 is investigated using temperature, wavelength and time as input parameters for the LED luminaire and is validated.

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“…However, the prediction accuracy of all the above-mentioned regression-based methods was compromised due to uncertainties in operating/loading conditions and discontinuities in data collection. Alternatively, Liu et al [19] used two different artificial neural network (ANN) model for predicting the lifetime of multi-chip high power LED light source. The first ANN was used to quantify the temperature distribution of high-power white LEDs from the finite element model (FEM) simulations and the second ANN was subsequently used for lifetime predictions.…”

Section: Introductionmentioning

confidence: 99%

Predicting Lumen Degradation of Light Emitting Diodes Using Hybrid Particle Filter Trained Neural Networks

et al. 2021

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High power white light emitting diodes (LEDs) are the widely opted eco-friendly alternate light source over incandescent lamps due to their lower power consumption and longer lifetime. The emerging market demand for LEDs brings about the critical need for adequate reliability testing and lifetime prognosis as they are predominantly used in uncontrolled environments. The common wear-out failure modes of LEDs include light output (luminosity) degradation and color shift. Commercially available LEDs have prolonged lifetime of about 70,000h which makes it impractical to obtain real-time degradation data, which subsequently complicates the ability to accurately predict the lumen maintenance life. Several attempts have been made by researchers to develop physics of failure models and / or data driven models for predicting the remaining useful life (RUL) of LEDs. However, these methods lack generalizability and do not address the prediction uncertainties caused by unit-to-unit variation. This calls for the need to develop robust prediction algorithms capable of handling variations in degradation trends due to manufacturing process, system design and environmental / operating conditions. This study proposes a hybrid prognostic approach which combines particle filters (PF) and neural networks (NN). The run-to-failure trend of one LED in a lot is used to model a feedforward neural network and the model parameters are optimized using the particle filter algorithm. The PF trained NN model is further used for RUL prediction for other LED devices in the lot enabling variations to be accounted for and naturally embedded into the prognostic framework. The accuracy of proposed hybrid approach was evaluated using RMSE as the performance metric.

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Lifetime prediction of a multi-chip high-power LED light source based on artificial neural networks

Cited by 20 publications

References 14 publications

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Comparison and validation of neural network models to estimate LED spectral power distribution

Predicting Lumen Degradation of Light Emitting Diodes Using Hybrid Particle Filter Trained Neural Networks

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