With their advantages of high efficiency, long lifetime, compact size and being free of mercury, ultraviolet light-emitting diodes (UV LEDs) are widely applied in disinfection and purification, photolithography, curing and biomedical devices. However, it is challenging to assess the reliability of UV LEDs based on the traditional life test or even the accelerated life test. In this paper, radiation power degradation modeling is proposed to estimate the lifetime of UV LEDs under both constant stress and step stress degradation tests. Stochastic data-driven predictions with both Gamma process and Wiener process methods are implemented, and the degradation mechanisms occurring under different aging conditions are also analyzed. The results show that, compared to least squares regression in the IESNA TM-21 industry standard recommended by the Illuminating Engineering Society of North America (IESNA), the proposed stochastic data-driven methods can predict the lifetime with high accuracy and narrow confidence intervals, which confirms that they provide more reliable information than the IESNA TM-21 standard with greater robustness.

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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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The introduction of high-power white LEDs has revolutionized the lighting industry in the past few decades due to the multiple benefits in terms of high reliability, environmental friendliness and versatile applications. However, challenges have arisen in assessing the reliability and lifetime prediction because it is difficult to record the failure data in a short period of time. Currently, the nonlinear least squares (NLS) regression-based method is used in industry for projecting the lumen maintenance lifetime from degradation data. The model parameters estimated using the NLS regression approach are deterministic and introduce high prediction errors. In this paper, a Bayesian method is proposed to estimate the remaining useful lifetimes (RULs) of both high-power white LED packages and lamps. The accelerated degradation tests conducted for gathering lumen degradation data are used to validate the proposed method. The exponential decay model is used as the degradation model and the parameters are estimated based on Markov Chain Monte Carlo (MCMC) sampling and using the Metropolis-Hasting (MH) algorithm. The lifetime prediction results showed that the Bayesian method has better prediction accuracy compared to the NLS method. Thus, the proposed Bayesian method is shown to be a promising approach to address the lifetime prediction issue for high-power white LEDs with improved prediction accuracy.

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The LED degradation failure is highly dependent on temperature and this degradation failure is an irreversible energy dissipation process in thermodynamics. In this paper, the entropy generation is used to quantify the energy dissipation, which is regarded as one of the main performance characteristics of LED's degradation process. Considering the thermodynamic characteristics of entropy generation in the LED failure, a hybrid degradation prediction model based on the permutation entropy (PE) and data-driven methods was proposed. Firstly, a thermal aging test was designed for white LEDs in which the entropy generation rates (EGRs) of LEDs were extracted from the online collected thermoelectric performance parameters. Then, the EGRs of LEDs were treated as a time-series signal to perform phase space reconstruction and calculate PEs. Finally, both neural network model and Wiener process based data-driven methods were used to process the PEs. This hybrid model links the thermodynamic entropy of LEDs with its optical performance. The results show that: (1) Entropy generation based on thermodynamics can characterize the degradation process of LEDs; (2) The proposed hybrid degradation prediction model based on the PE and Wiener method can achieve early failure warning of LEDs before the actual failure occurs.@en

Ultraviolet light-emitting diodes (UV LEDs) play an important role in inactivating novel coronavirus pneumonia, but the lack of rapid lifetime prediction can easily cause untimely failure detection, long product development cycles, and high costs. This study predicts the lifetime of UV LEDs based on the long short-term memory (LSTM) recurrent neural network (RNN). First, the equipment setup was designed to conduct an aging test to obtain a predicted length of life for the UV LED samples using a Weibull distribution. Next, LSTM RNN was employed to predict the lifetime of the UV LEDs based on the radiation power degradation. The results were then compared with those from nonlinear least squares (NLS) regression recommended by the IESNA TM-21 industry standard. Finally, the robustness of the two methods was analyzed by changing the starting times of the predictions. The results showed that the LSTM RNN proposed in this letter reveals not only a 29.7% lower lifetime prediction error compared with the NLS regression, but also a more stable robustness. Thus, the LSTM RNN method is found to be more accurate and more robust in predicting the lifetime of UV LEDs.

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The estimation of lifetime for highly reliable products including Ultraviolet Light-emitting Diodes (UV LEDs) has been challenging based on traditional lifetime tests that records time to failure. Recently constant stress and step stress degradation tests are used to gather degradation path data and modeling the degradation of performance characteristics has been applied. In this paper, a step stress accelerated degradation test (SSADT) designed to capture the degradation path and study the lifetime of UV LEDs. The radiation power degradation path was analyzed based on the IES TM-21 least square regression (LSR) and Wiener process approach. With its advantage of requiring smaller sample size and shorter test time, the SSADT provides a degradation path suitable for the proposed Wiener process modeling. The lifetime estimation for UV LEDs based on the proposed wiener process approach shows better prediction accuracy compared to the TM-21 LSR approach. By using this method, dynamic changes and degradation of the UV LEDs can be easily studied, and it can effectively estimate their remaining useful lifetimes.

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