In this research, drones were used to capture thermal images and detect different types of failure of solar modules, and MATLAB® image analysis was also conducted to evaluate the health of the solar modules. The processes included image acquisition and transmission by drone, grayscale conversion, filtering, 3D image construction, and analysis. The analyzed targets were the solar modules installed on buildings. The results showed that the employment of drones to monitor solar module farms could significantly improve inspection efficiency. Moreover, by combining the mean and median filtering techniques, an innovative box filtering method was successfully created. Additionally, this study compared the differences between the mean, median, and box filtering techniques, and proved that the 3D image improved by box filtering is a more convenient and accurate way to check the health of solar modules than the mean and median filtering methods. In addition, this new method can simplify the maintenance process, as it helps maintenance personnel to determine whether to replace the solar modules on site, achieving the goal of power generation efficiency enhancement. It is worth noting that 3D image recognition technology can enhance the clarity of thermal images, thereby providing maintenance personnel with better defect diagnosis capability. It is also able to provide the temperature value of the defect zone, and to indicate the scale of defects through the cumulative temperature chart, so the 3D image is qualified as a quantitative and qualitative indicator. The analysis of the transmitted image is innovative that it not only can locate the defect area of the module, but also can display the temperature of the module, providing more information for maintenance personnel.

Recently, artificial intelligence models have been developed to simulate the biomass gasification systems. The extant research models use different input features, such as carbon, hydrogen, nitrogen, sulfur, oxygen, and moisture content, in addition to ash, reaction temperature, volatile matter (VM), a lower heating value (LHV), and equivalence ratio (ER). The importance of these input features applied to artificial intelligence models are analyzed in this study; further, the XGBoost regression model was used to simulate a biomass gasification system and investigate its performance. The top-four features, according to the results are ER, VM, LHV, and carbon content. The coefficient of determination (R2) was highest (0.96) when all eleven input features noted above were selected. Further, the model performance using the top-three features produced a R2 value of 0.93. Thus, the XGBoost model performance was validated again and observed to outperform those of previous studies with a lower mean-squared error of 1.55. The comparison error for the hydrogen gas composition produced from the gasification at a temperature of 900 °C and ER = 0.4 was 0.07%.

The causes of the fracturing of stone grills in barbecue ovens were analyzed using a coupled analysis model that combined combustion thermodynamics with heat conduction and the finite element method. The proportion of mixed air for combustion was simulated, and the stone grill plate temperature and thermal stress distribution were the two factors taken to enhance the design of stone grill plates. Moreover, thermal images were used to compare the performance of the original and the improved plates to quantify the improvements, and validate the accuracy of the simulations. The results showed that the temperature distribution was uniform across the stone grill plate. When comparing the simulation model and the actual experiment, the simulation model can generate an optimal design with fewer errors in a shorter period. The combustion tube is deemed to have considerable influence on the performance of the barbecue oven. The surface temperature distribution of the stone grill plate was improved by controlling the amount of fuel entering the combustion system and/or changing the material and shape of the stone grill. On the other hand, the analysis results of the improved stone grill plate in this study showed that we can correct the temperature difference and thermal stress difference caused by the opening of the upper cover of the oven. According to our study, the average thermal stress on the surface of the stone grill plate was effectively reduced by 45.3 MPa. The average temperature difference decreased by 91 °C. At the same time, by improving the intake position and method of the combustion tube, the air mass flow in the combustion tube increased by 12%, which effectively improved the combustion efficiency of the combustion tube. In particular, a more uniform distribution was achieved by decreasing the temperature of the mixed air entering the combustion tube, which in turn increased the flow rate and velocity of the air flowing through the top of the flame to the bottom surface of the stone grill plate. The strategies employed can prevent the thermal-stress-induced fracturing of stone grill plates and prolong their service life.

In recent years, artificial intelligence (AI) technology has been applied in different research fields. In this study, the XGBoost regression model is proposed to estimate JT9D engine thrust. The model performance mean absolute error (MAE) is 0.004845, the mean-squared error (MSE) is 0.000161, and the coefficient of determination (R2) values of the training, validation, and testing subsets are 0.99, 0.99, and 0.98, respectively. Based on a model sensitivity analysis, the four parameters’ optimal values are as follows: the number of estimators is 900; the learning rate is 0.1; the maximum depth is 4, and the random state is 3. In addition, a comparison between the model performance in this study and that in a previous one was conducted. The MSE value is as low as 0.000021.