In industrial environments, motor condition monitoring often faces problems such as complicated wired installation and low efficiency in manual inspection. To solve these issues, this paper designs a motor condition monitoring and fault early warning system based on LoRa. The system adopts STM32F103 as the main controller, and uses ADXL345 accelerometer and DS18B20 temperature sensor to collect real-time vibration and temperature signals of the motor. A star-shaped wireless sensor network is established through LoRa modules to realize long-distance data transmission. A host computer program is developed in LabVIEW for data display, recording and management. In addition, a multi-level alarm strategy based on threshold detection is applied in the system, which can trigger alarms automatically when abnormal parameters are detected. Test results indicate that the packet loss rate is less than 3 % within 500 meters, the temperature measurement error is controlled within ± 0.5° C, and the vibration measurement accuracy satisfies industrial application requirements. During 24 hours of continuous multi-node concurrent operation, the system maintains an average data integrity rate of 99.2 %. The proposed system can effectively realize remote monitoring and early fault warning of motor working status, and has strong practical value in industrial applications.

Aiming at enhancing the capture performance of electrostatic precipitator (ESP) for PM2.5 particles, this paper introduces a multi-field coupling model and carries out the analysis of the electromagnetic dedusting mechanism of four electric field ESP under the magnetic-field-induced trajectory modification, and investigates the capture performance of multiple electric field ESP for PM2.5 particles at different magnetic field introduction positions through numerical simulations and PIV (Particle Image Velocimetry) experiments. The PIV measurements showed flow-field patterns that were consistent with established ESP findings, where higher applied voltage and lower flue-gas velocity lead to stronger particle deflection toward the collection plate. These observed trends serve as a validation of the reliability of the present PIV setup and simulation model. Introducing a magnetic field into a specific zone resulted in particles to follow a spiral path leading to the collection plate. This motion not only altered the paths of particles in the targeted region but also influenced adjacent zones. Magnetic confinement was found to be more effective under low flue gas velocity and low operating voltage conditions. Furthermore, at lower voltages, the third electric field was more sensitive to magnetic confinement, whereas at higher voltages, the fourth electric field exhibited greater sensitivity. The findings can offer new design ideas for performance enhancement of traditional ESPs.

Electrostatic cyclone precipitator (ECP) has a long history in industrial dust management. However, as controlling over smoke emissions becomes increasingly stringent, traditional dust-removal equipment is no longer able to meet existing emission standards. Aiming at improving the collection performance of ECP for trapping fine particles, this study optimizes the spherical cylindrical ECP by introducing a multi-field coupling model and altering the exhaust port internal probing depth. The influence of exhaust port internal probing depth on the collection performance of the ECP under magnetic confinement effect was investigated. The results indicate that the exhaust port internal probing depth greatly affects the particle trajectory and electric potential distribution in the ECP. With the increase of the exhaust port internal probing depth, the increase of working voltage and magnetic induction intensity gradually shifts the maximum point of the overall efficiency curve forward. Under the practical industrial conditions with flue gas velocity of 20 m/s, working voltage of 30 kV and magnetic induction intensity of 0.5 T, the exhaust port internal probing depth making trapping performance reach the optimal value is 85 mm, which results in a maximum collection efficiency of 91.2 %. This is an improvement of 23.7 % compared to the unoptimized configuration. With the rise of operating voltage and magnetic induction intensity, trapping performance of the optimized ECP for fine particles enhances, but the enhanced magnitude of dust-removal efficiency decreases. The findings can offer new design ideas for performance enhancement of traditional ECPs, and providing new directions for reducing industrial smoke and dust emissions.

Electrostatic precipitators (ESPs) are widely used for industrial particulate control but exhibit limited efficiency for PM2.5, especially submicron particles. This study proposes an enhanced ESP with auxiliary electrodes and magnetic field. A multi-physics numerical model evaluated synergistic effects across electrode spacings of 0.018–0.044 m under the selected model assumptions and operating conditions. Magnetic effects were more pronounced for particles <0.5 μm. Spacing analysis reveals a non-monotonic trend for auxiliary electrodes and monotonic decline in magnetic enhancement with wider gaps within the investigated parameter range. These findings guide high-efficiency ESP designs for ultrafine particulate control.

Emission standards are becoming increasingly strict for large emitters such as coal-fired power plants, while high-temperature precipitators can effectively reduce polluting emissions. This work introduced a ++n external magnetic field into the electrostatic precipitator (ESP) to enhance the collection efficiency of a high-temperature wire-plate ESP for PM2.5. Then a multi-physics field theoretical model was established, including electromagnetic, temperature, fluid, and particle dynamic fields. Based on this model, numerical simulations were performed to evaluate the collection performance of PM2.5 with R-R size distribution. The results indicate that PM2.5 collection efficiency presents a nonlinearly decreasing trend with increasing temperature, while the external magnetic field can improve the collection efficiency of PM2.5, and the promoted effect of magnetic field on collection efficiency is more significant in high temperatures. These findings mean that the external magnetic field in wire-plate ESP has important reference values for improving the collection performance of fine particles..

Intelligence algorithms used to predict the conventional life of photoelectric products significantly reduce the time and cost of product life test, which is a popular life evaluation method in the photoelectric industry. However, the accuracy of prediction results remains a challenge for most existing models. In this study, three innovative high-precision life prediction models for photoelectric products were first proposed based on the particle swarm optimization-support vector machine (PSO-SVM) algorithm combining ALA. Then for the sake of validating the accuracy of these models, luminance attenuation data of a certain plant lighting LED lamp were collected through our self-developed accelerated life test system, and input into PSO-SVM as training data after being processed according to a self-designed data preprocessing scheme. Eventually, three models were respectively applied to predict the conventional life of the plant lighting LED lamp. The results demonstrate that the PSO-SVM life prediction models proposed in this work exhibit a high accuracy in predicting conventional life of photoelectric products, with the minimum relative error of only 0.74% compared with the referred conventional life value. The above-mentioned achievements can provide a new approach for evaluating modern photoelectric product life.

The practice of establishing a life prediction model from accelerated life test (ALT) data to assess products' reliability is becoming increasingly popular in industries producing long-life products such as solid-state light-emitting devices (solid-state LEDs). Nevertheless, if ALT fails and results in data missing, retesting will cost a lot of time and affect the production cycle. To address this issue, a life prediction model based on support vector machine (SVM) improved by adaptive genetic algorithm (GA) was proposed in this study. First, adaptive GA-SVM was used for the restoration of failure ALT data, and then the conventional life of the product could be predicted according to the restored data combined with probabilistic statistical methods. Furthermore, in order to verify the prediction performance of the model, the failure ALT of an organic light-emitting diode (OLED) product was taken as an example. The results demonstrate that the data restoration effect of the model is satisfactory, and the relative error between the life predicted according to the restored data and the actual life of the product is merely 2.88%, which proves the effectiveness of the model proposed. Meanwhile, the achievement provides a good remedy for the special situation of failed ALT.

The electrostatic cyclone precipitator (ECP) has been widely utilized in industry to control particle escape. However, the dust-removal performance of submicron particles which are the most difficult to be collected in traditional purification technologies cannot meet increasingly strict environmental standards. In this work, the cyclone precipitator was improved through changing vortex, a coupling theoretical model was built, and the dust-removal performance of submicron particles in an improved ECP under different flue gas velocities and magnetic induction intensities was investigated. It is found that compared to cyclone precipitator, the dustremoval efficiency in an improved ECP is significantly promoted with a maximum increase of 52.7%. At the submicron scale, as the particle size increases, the dust-removal performance of the improved ECP declines. The collection performance of submicron particles with or without the magnetic field gradually increase with the drop of the flue gas velocity. The improved range of dust-removal efficiency caused by magnetic confinement effect can reach 7.7% - 10.8%. At the same time, the higher the flue gas velocity, the greater the enhancement of the dust-removal efficiency caused by magnetic field. The results can provide new ideas for ECP structural improvement and insights into the profound removal of submicron particles caused by the combustion of fossil fuel.

For the purpose of improving the life accuracy calculated based on accelerated degradation data of light-emitting diodes (LED) lights, three-parameter Weibull right approximation method (TPWRAM) was applied to fit four groups of degradation data from accelerated degradation tests (ADTs) to obtain luminance degradation curves, and the pseudo-failure time of each sample was obtained combined with failure criteria. Then the life distribution under each stress was described by lognormal distribution, the maximum likelihood estimation (MLE) was employed to calculate the distribution parameters, and the accelerated average life was acquired. Finally, the LED light life under conventional stress was predicted by the inverse power law. The proposed life prediction method was also applied to predict the life of vacuum fluorescent display to verify the applicability of the proposed method to other data. The results show that the data obtained via the four groups of ADTs are highly reliable and the life distribution of LED is completely in line with the lognormal distribution. Besides, the determination coefficient R2 of the LED life characteristic curve is 0.9841, which is close to 1, meaning that the conventional life prediction of LED is accurate. Then the result of applying the same method to the life prediction of vacuum fluorescent display (VFD) shows that the relative error between the predicted value and the actual conventional life of VFD is only 1.44 %, which proves that the proposed life prediction method also has good applicability to other photoelectric device. The proposed life prediction method can provide some guidance for the related standards establishment of the LED light life.

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 degrees degrees 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.

In order to further improve the trapping effect of fine particles, an improved electrostatic cyclone precipitator (ECP) was proposed. The electromagnetic dust-removal mechanism of spherical cylindrical ECP was revealed, and the influences of flue gas velocity on the dust-removal effect of fine particles with and without magnetic confinement effect were discussed. The results show that the overall efficiency curve of fine particles shows a ' hump ' type with the change of flue gas velocity, and the increase of magnetic induction intensity promotes the hump to move to the low flue gas velocity area. Increasing magnetic induction intensity can improve the trapping performance of spherical cylindrical magnetically constrained ECP, and the improvement effect weakens gradually when the same amplitude increases.

Aiming at the problem that the cycle life of the battery is hard to be accurately estimated, a segmented capacity degradation model is established based on the trend of capacity degradation rate. By applying a genetic algorithm (GA) to optimize the model parameters, GA-Weibull and GA-SVR (support vector regression) degradation models and their corresponding segmented models are established, and the battery life is calculated. Furthermore, a method taking the RMSE as the target is put forward to calculate the knee-point of capacity degradation. Finally, the life is predicted by segmented models. The results show that compared with unsegmented models, describing the process of capacity degradation by segmented models reduces the error notably, makes the determination coefficient closer to 1, and improves the model accuracy effectively. Later, the proposed method makes the fitting error of the segmented model smaller than that of Bacon-Watts method, thus obtaining a more accurate knee-point. In addition, it is found that there exists linear relationship between the estimated knee-point and the battery life. Finally, the prediction results based on segmented model show that the prediction accuracy of segmented GA-SVR model is higher, and the MAPE is only 4.32%. The relevant results can provide some guidance for the reliability evaluation and product quality management of lithium-ion batteries, which is conducive to establishing more accurate models for battery life prediction.

In order to improve the mechanical property prediction accuracy of AZ80 magnesium alloy, sparrow search algorithm (SSA) is optimized by the tent chaotic mapping (TCM) algorithm, yielding TCMSSA, and TCMSSA is employed to improve extreme learning machine (ELM) model for obtaining TCMSSA-ELM model. TCMSSA-ELM model, along with the traditional ELM model, are both utilized to predict the stress of AZ80 magnesium alloy in a temperature range of 523 K to 673 K and a strain rate range of 0.001s− 1 to 1s− 1. Comparative analysis is conducted to evaluate the performance differences between the two models. The results indicate that compared with ELM model, the predicted values of TCMSSA-ELM model are closer to the experimental data and the ideal 45° line, proving the higher prediction accuracy of TCMSSA-ELM model. In addition, through algorithmic improvements, the maximum reduction of MAPE is 85.422

In order to increase the prediction accuracy of the peak of flow stress for the 6016H18 aluminum alloy, the Browman constitutive model is optimized by quadratic polynomial fitting instead of linear fitting, and back propagation (BP) is improved by active target particle swarm optimization (ATPSO) to obtain the ATPSO-BP model. The peak stress of the 6016H18 aluminum alloy is predicted and comparatively analyzed. According to the numerical results, the prediction precision of the optimized Browman constitutive model is higher than that of the original one. It is confirmed by the mean relative error that the ATPSO-BP model performs better in terms of prediction than the Browman constitutive model, the optimized model, and the BP model, which can objectively reflect the influence of all heat deformation parameters on peak stress. The related results can provide an important theoretical basis for studying the mechanical properties of aluminum alloy and provide a technical reference for the application of the 6016H18 aluminum alloy.

In recent years, with the improvement of people’s living standards, lamps have played an irreplaceable role as an essential part of daily life. As an important indicator, the lifetime of lamps directly affects their reliability and performance stability. In order to quickly and reliably determine the conventional lifetime of light-emitting diode (LED) lamps, four sets of constant-stress accelerated degradation tests have been performed on LED lamps. The luminance degradation test data were fitted using the three-parameter Weibull right approximation method (TPWRAM), and each sample’s pseudo-failure time was calculated by combining with the failure criteria. Ultimately, the life prediction of LED lamps was achieved by using the least square method (LSM) and the probabilistic life prediction model under Weibull distribution. The results indicate that the overall average mean absolute percentage error (MAPE) of the fitting curve of luminance degradation data for LED lamps by TPWRAM is only 5.024%, the determination coefficient of the fitting line from the pseudo-failure time based on Weibull distribution and LSM is close to 1, as well as the adjusted determination coefficient, and the residual standard deviation is close to 0, proving that the probabilistic model established by Weibull distribution has high accuracy in predicting conventional life. The luminance degradation experiment and the life prediction model of LED lamps can provide important guidance for life prediction of other photoelectric devices.

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 PM2.5, seriously affecting human health. To investigate the PM2.5 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 PM2.5 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 mu m 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.

Purpose With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties. Design/methodology/approach Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance. Findings The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model. Originality/value The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

Aluminum alloy is widely used in daily life due to their good properties. In order to get the change rule of the mechanical properties of 6181H18 aluminum alloy, a detecting particle swarm optimization (DPSO) algorithm was adopted to update weights and thresholds of back propagation neural network (BP NN) in an innovative way. In this way, a DPSO-BP NN prediction model was established to improve the prediction accuracy and was applied to predict the peak stresses of 6181H18 aluminum alloy. The results show that the predicted values obtained based on BP NN and DPSO-BP NN are both very close to the experimental ones and they can reflect the variation law of the stresses of 6181H18 aluminum alloy. It is confirmed that the DPSO-BP NN has a higher prediction accuracy by the mean relative error, standard residual, R-squared and root mean square error (RMSE). The established DPSO-BP NN prediction model owns better prediction capability compared with the traditional BP NN model. The results of this study can provide a scientific basis for the improvement of mechanical properties of alloy materials, and offer a technical reference for technical workers in related fields.