Jianping Zhang

Four groups of accelerated degradation tests of light-emitting diode (LED) lights under constant current stresses were carried out with the goal of precisely and rapidly obtaining the conventional life of LED lights. Three-parameter Weibull function and right approximation method were used to fit the test data of the luminance degradation with time of samples under different stresses, and the pseudo-failure time of each sample was calculated according to the failure criteria. Then combined with lognormal distribution and least square method, the life distribution of LED lights under each stress was determined, and the life of LED lights under conventional stress was extrapolated by inverse power law. The results show that the obtained luminance degradation data can reflect the decay trend of LED lights preferably and obey the three-parameter Weibull function, the lognormal distribution has a good fitting effect on the failure time distribution of LED lights, and the conventional life predicted by the model combined with Weibull function and lognormal distribution has a high accuracy. Manufacturers and researchers of LED lights can benefit from theoretical foundation and technical reference offered by the life tests of LED lights and the established life prediction model.

With the rapid development of industry, coal-fired power generation accounts for a large proportion of the total power generation, emitting a large amount of harmful substances, such as PM_2.5, seriously affecting human health. To investigate the PM_2.5 collection efficiency of wire-plate electrostatic precipitator (ESP) at different temperatures, numerical simulations based on the multi-field coupling model of ESP were conducted. Support vector machine (SVM) model combined with particle swarm optimization (PSO) algorithm gives the PSO-SVM prediction model, and the simulated data are used as training data, PSO-SVM and back propagation neural network (BPNN) models are used to predict the temperature effect under different operating conditions. The results show that PM_2.5 collection efficiency in the wire-plate ESP gradually decreases with increasing temperature, and the decreased rate becomes small constantly. Both PSO-SVM and BPNN models accurately describe the relationship between collection efficiency and temperature, the average relative errors of the two models for predicting the collection efficiency of 1.0 μm particles at different temperatures are 0.247% and 0.363%, respectively. Compared with BPNN, the overall error of PSO-SVM is 0.928% lower, suggesting that PSO-SVM model yields smaller relative error and higher prediction accuracy. The related findings can provide references for studying the collection performance and rapidly determining the operating parameters of ESP.

The mechanical properties of magnesium alloy in different molding stages are very important factors to determine its application approaches in engineering. In order to ensure the prediction accuracy of mechanical properties, a TCMSSA-ELM model, which is a hybrid of the sparrow search algorithm (SSA) optimized by the tent chaotic mapping (TCM) algorithm and the extreme learning machine (ELM), is proposed in this study, and the stresses of AZ80 magnesium alloy are predicted by the model through a 812-record dataset. The predicting results indicate that TCMSSA improves the accuracy of ELM model. Compared with ELM model, the data points formed by experimental values of stress and the predicted ones by TCMSSA-ELM model are closer to the ideal 45° line, the average determination coefficient rises by 1.43%, and the average root mean squared error (RMSE) decreases by nearly 61.96%, implying that TCMSSA-ELM model accurately reflects the influence rule of thermodynamic parameters on stress. The novelty of this study is that TCM is used to optimize the population initialization of SSA, which enables SSA to have a higher global search ability, and thus optimizes the weight and threshold selection of ELM, then making TCMSSA-ELM have higher prediction accuracy than other improved ELM models.

To accurately and promptly obtain the conventional lifetime of light-emitting diode (LED) lights, accelerated degradation tests of LED lights under four groups of accelerated stresses were designed and carried out. Three-parameter Weibull function and the right approximation method were used to fit the average luminance degradation test data at each constant accelerated current stress. Combined with the failure criteria, the service lifetime of the samples under each accelerated stress condition was calculated. Then, four groups of accelerated degradation data points were extrapolated by power function and exponential function respectively, and the average lifetime of LED lights was calculated, thereby achieving the estimation of conventional lifetime. The results show that the luminance degradation rate of LED lights is initially fast, then slow, which accurately represents the basic rule of luminance decay. The model established by the Weibull right approximation method exhibits high fitted accuracy for the luminance degradation data of LED lights. By comparing the determination coefficients and the mean absolute percentage errors of extrapolation, it can be found that the conventional lifetime extrapolated by power function has superior accuracy, satisfying the actual project requirements. The relevant research results can provide technical support for the life prediction of LED lights.

Charged particles have high momentum under high-temperature conditions, which helps to promote their movement towards a dust collector in a magnetic field environment, making it possible to improve the efficiency of the high-temperature wire-plate electrostatic precipitator (ESP) in this environment. A multi-field coupling model was established to numerically simulate PM2.5 dust-removal efficiency in an ESP under different working conditions. Combining the particle swarm optimization (PSO) algorithm with the support vector machine (SVM) model, the PSO-SVM prediction model is presented. Simulated data were used as training data, and PSO-SVM and back-propagation (BP) neural network models were utilized to predict collection efficiency under different working conditions, respectively. The results show that introducing a magnetic field can effectively improve the PM2.5 collection efficiency of wire-plate ESP, and the effect of a magnetic field on the dust-removal efficiency is more obvious at higher temperatures and higher flue gas velocities. When changing the working conditions, the predicted results of the magnetic field effect conform to simulated ones, and the PSO-SVM predicted values have a smaller relative error than those of the BP model, which can better adapt to different working conditions. All of the above conclusions can be utilized as a simple and adequately efficient example of the ESP model for follow-up research.