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Grafting nanomaterials on the carbon fiber (CF) surface is considered an effective strategy for enhancing the interfacial properties of CF-reinforced polymer composites (CFRPs). However, the mechanical properties of the CFs are often compromised during treatment. A new method for in situ growing silver nanoparticles (AgNPs) on the CF surface is proposed in this study. The CFs are first immersed in a low-viscosity silver nitrate solution to form a thin liquid film on the surface. Subsequently, using the abundant active particles in the plasma, the silver ions are reduced to silver atoms and grown into AgNPs on the CF surface. The tensile strength of CF@Ag was 38.74% greater than that of untreated CF, potentially due to the reparative action of AgNPs on defects in CF. The CF showed an evident improvement in surface wettability because of the AgNPs. Furthermore, the interfacial properties were noticeably improved, with the interfacial shear strength of CF@Ag increasing to 91.59 MPa, which was about twice that of pristine CF. Thus, the mechanical properties of composites are significantly improved (flexural strength increased by 190.74%). This study presents a non-destructive and convenient method for growing nanoparticles onto CF to establish a robust interface in CFRPs. [ABSTRACT FROM AUTHOR]

Cable terminal defect detection plays an important role in ensuring the safe and stable operation of high-speed trains. In this paper, a numerical model of the electromagnetic thermal field of overheating defects of cable terminal shielding grids and a method of detecting internal defects of cable terminals—even-order temperature derivative is proposed for quantitative detection of internal defective structures of vehicle-mounted cable terminals. Firstly, a numerical model of the electromagnetic thermal field of cable terminals under the condition of leakage current is constructed, through which the temperature field distribution characteristics of different defective structures are analyzed. Then, the intuitive location of the defective region is obtained by investigating the derivative characteristics of the temperature image, and the depth and intensity of the defects are quantitatively assessed by using the main side peak (MSP) distances and the MSP values extracted from the derivative curves. Finally, the simulation and experimental results achieve the identification of defect structures and the quantitative detection of defect depth and intensity, proving the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals. [ABSTRACT FROM AUTHOR]

Accurate prediction of lithium ion (li-ion) battery capacity is of great significance to battery health status management. In this paper, the different discharge time corresponding to the equal voltage interval is taken as the health factor. Three highly correlated health factors are extracted from the battery discharge curve, and the Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) is used to estimate the battery capacity accurately and quickly. Firstly, health factors related highly to battery capacity from the battery cycle life test are extracted, and the selected health factors are analyzed using the Spearman correlation coefficient and Pearson correlation coefficient. Secondly, this paper analyzes the prediction effect of different combinations of selected health factors using BPNN optimized by a genetic algorithm. Then, to verify the superiority of the proposed optimization algorithm, different optimization algorithms are used to adjust and optimize the parameters of BPNN automatically, and the experimental data are used for comparative analysis. Finally, the GA-BP is compared with other common battery capacity prediction methods. The results show that the GA-BP neural network prediction model can accurately and effectively predict the capacity of li-ion battery when using selected health factors. [ABSTRACT FROM AUTHOR]

Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models. [ABSTRACT FROM AUTHOR]

The acidic environment is one of the main factors leading to the aging of silicone rubber (SiR) insulators. Aging can reduce the surface hydrophobicity and pollution flashover resistance of insulators, threatening the safe and stable operation of the power grid. Therefore, evaluating the aging state of insulators is essential to prevent flashover accidents on the transmission line. This paper is based on an optical hyperspectral imaging (HSI) technology for pixel-level assessment of insulator aging status. Firstly, the SiR samples were artificially aged in three typical acidic solutions with different concentrations of HNO 3 , H 2 SO 4 , and HCl, and six aging grades of SiR samples were prepared. The HSI of SiR at each aging grade was extracted using a hyperspectral imager. To reduce the calculation complexity and eliminate the interference of useless information in the band, this paper proposes a joint random forest- principal component analysis (RF-PCA) dimensionality reduction method to reduce the original 256-dimensional hyperspectral data to 7 dimensions. Finally, to capture local features in hyperspectral images more effectively and retain the most significant information of the spectral lines, a convolutional neural network (CNN) was used to build a classification model for pixel-level assessment of the SiR's aging state of and visual prediction of insulators' defects. The research method in this paper provides an important guarantee for the timely detection of safety hazards in the power grid.