The safety of composite insulators in high-voltage transmission lines is directly related to the stable operation of the power system, which is a fundamental condition for the normal functioning of people’s lives and industrial production. Composite insulators are exposed to outdoor conditions for extended periods of time, and with the increase in service life, they are subjected to aging due to external environmental factors and electrical stresses. This aging leads to a decline in their electrical insulation, mechanical properties, and other performance, which, in severe cases, may result in power system failures. Therefore, accurate assessment and detection of the aging status of composite insulators are particularly important. Traditional detection methods such as visual inspection, hardness testing, and hydrophobicity testing have limitations, including single functionality and susceptibility to environmental interference, which cannot comprehensively and accurately reflect the aging condition of the insulators. In recent years, spectroscopy-based detection technologies have been increasingly applied for the rapid detection of composite insulators due to their advantages, such as high sensitivity, non-contact measurement, and multi-dimensional information extraction. Common spectroscopic detection methods include Ultraviolet Discharge (UV Discharge), Fourier Transform Infrared (FTIR) Spectroscopy, Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Laser-Induced Breakdown Spectroscopy (LIBS), and Terahertz (THz) Spectroscopy. These methods offer non-contact, remote, and rapid capabilities, enabling detailed analysis of the insulator’s surface microstructure, chemical composition, and aging characteristics. This paper introduces = spectroscopy-based methods for detecting the aging status of composite insulators, analyzing the advantages and limitations of these methods, and discussing the challenges of their industrial application. Furthermore, the paper reviews the research progress and practical applications of spectroscopic techniques in the evaluation of insulator aging status, systematically summarizing important achievements in the field and providing an outlook for future developments.

Water quality should be tested to ensure it is acceptable for the healthy growth of plants and animals, and water hardness is one of the important testing indexes. Herein, a novel approach was proposed to achieve high accuracy and rapid quantitative analyses of water hardness by combining one-point calibration laser-induced breakdown spectroscopy (OPC–LIBS) and aerosolization. First, the water samples are aerosolized via the aerosol generation device and the LIBS spectra of aerosols are obtained. Then, a modified OPC–LIBS model is used to determine the elemental contents of the aerosols via LIBS spectra, in which the plasma temperature is calculated using the Multi-Element Saha–Boltzmann (ME–SB) plot. One suitable standard liquid sample (the concentrations of Ca, Mg, and Sr were 50 mg/L, 50 mg/L, and 500 mg/L, respectively) was selected to evaluate the quantitative performance of the modified OPC–LIBS. Then, the Ca and Mg concentrations in the three real water samples (from the Yangtze River, reservoir, and underground) were detected and quantified by the proposed method, and the quantitative results of three LIBS calibration methods were compared with that of inductively coupled plasma optical emission spectroscopy (ICP–OES). The average relative error of Ca and Mg found in the OPC–LIBS results was lower by 22.23% than the internal standard method and 14.50% lower than the external standard method. The method combining modified OPC–LIBS and aerosolization can achieve high-precision rapid quantification of water hardness detection, which provides a new path for rapid detection of water hardness and is expected to make online detection a reality in the water quality testing field.

To address the challenges of significant size variations, inconspicuous textures, weak edges, and background interference in wire harness insulation surface defect detection, this paper proposes WHD-YOLOv8n, an improved YOLOv8n-based defect detection algorithm that integrates ODConv, CBAM, BiFPN, and Inner-CIoU for adaptive feature extraction, defect-related feature enhancement, multi-scale feature fusion, and bounding-box regression, respectively. WHD-YOLOv8n achieves an mAP@0.5 of 90.15%, representing a 6.21 percentage-point improvement over the YOLOv8n baseline, with Precision, Recall, and F1-score of 92.05%, 89.87%, and 90.95%, respectively. The model has 4.83 M parameters and requires 11.9 GFLOPs, while achieving an inference speed of 135.6 FPS. Ablation experiments and comparisons with representative general-purpose and industrial defect detectors demonstrate the effectiveness of the proposed design and its favorable accuracy–efficiency trade-off. Further experiments under haze and fog degradation show that WHD-YOLOv8n consistently outperforms the baseline under the tested degraded imaging conditions, although severe degradation can still cause missed detections. The proposed method therefore provides an effective solution for wire harness insulation surface defect detection and offers a basis for further research on robust detection under complex industrial imaging conditions.