This study introduces dynamic vision sensors (DVS) in LIBS for the first time, and a temporal–spatial attention fusion network is proposed to extract and cross-modally fuse features from LIBS spectral data and plasma images.

Laser-induced breakdown spectroscopy (LIBS) is widely used for online quantitative analysis in industries due to its rapid analysis and minimal damage. However, challenges like signal instability, matrix effects, and self-absorption hinder the measurement accuracy. Recent approaches, including the internal standard method and crater limitation method, aim to improve the stability but suffer from high computational demands or complexity. This study proposes a method to enhance LIBS stability by utilizing craters formed from laser ablation without external cavity assistance. It first improves the plasma temperature calculation reliability using multiple elemental spectral lines, after which electron density calculations are performed. By fitting plasma parameter curves based on laser pulse counts and using a laser confocal microscope for crater analysis, stable plasma conditions were found within crater areas of 0.400 mm² to 0.443 mm² and depths of 0.357 mm to 0.412 mm. Testing with elemental spectral lines of Ti II, K II, Ca I, and Fe I showed a significant reduction in the relative standard deviation (RSD) of the LIBS spectral line intensity, demonstrating an improved signal stability within specified crater dimensions.

The optimal experimental parameters for LIBS and DVS were determined. Achieved high-quality reconstruction of plasma images by event data from DVS data. Established the DVS-SC model, enhancing the accuracy of LIBS analysis.

Sheet Molding Compound (SMC) materials are extensively utilized as high-voltage insulation materials in electrical equipment. SMC materials are prone to aging after long-term operation. Conducting non-destructive testing to assess their electrical and physicochemical properties is crucial for the safe operation of electrical equipment. This study identifies the optimal equipment parameters for testing SMC materials using Laser-Induced Breakdown Spectroscopy (LIBS) technology through experimental investigation and also explores the ablation characteristics of SMC under various laser parameters. The results indicate a significant positive correlation between the ablation depth and laser pulse number, while there is no correlation with single laser pulse energy. However, the ablation area demonstrates a strong positive correlation with both single laser pulse energy and laser pulse number. Additionally, LIBS spectral analysis provides elemental results comparable to Energy Dispersive Spectroscopy (EDS), facilitating the examination of variations in Na, Ti, Fe, Mg, Ca, and C elemental contents with depth. Moreover, an enhanced iterative Boltzmann plot method is suggested for calculating the plasma temperature using 21 Fe I spectral lines and the electron density using the Fe II 422.608 nm line. The variations of these plasma parameters with laser pulse number are documented, and the results show consistent trends, confirming that the laser-induced SMC plasma adheres to local thermodynamic equilibrium.

The electric energy metering box serves as a crucial node in power grid operations, offering essential protection for key components in the distribution network, such as smart meters, data acquisition terminals, and circuit breakers, thereby ensuring their safe and reliable operation. However, the non-metallic housings of these boxes are susceptible to aging under environmental stress, which can result in diminished flame-retardant properties and an increased risk of fire. Currently, there is a lack of rapid and accurate methods for assessing the fire resistance of non-metallic metering box enclosures. In this study, laser-induced breakdown spectroscopy (LIBS), which enables fast, multi-element, and non-contact analysis, was utilized to develop an effective assessment approach. Thermal aging experiments were conducted to systematically investigate the degradation patterns and mechanisms underlying the reduced flame-retardant performance of sheet molding compound (SMC), a representative non-metallic material used in metering box enclosures. The results showed that the intensity ratio of aluminum ionic spectral lines is highly correlated with the flame-retardant grade, serving as an effective performance indicator. On this basis, a one-dimensional convolutional neural network (1D-CNN) model was developed utilizing LIBS data, which achieved over 92% prediction accuracy for different flame-retardant grades on the test set and demonstrated high recognition accuracy for previously unseen samples. This method offers significant potential for rapid, on-site evaluation of flame-retardant grades of non-metallic electric energy metering boxes, thereby supporting the safe and reliable operation of power systems.

Intumescent fireproof coatings protect steel structures and cables by forming a thick, fire-resistant layer under high temperatures. These coatings can deteriorate over time, impacting their fire resistance. Current testing methods are largely lab-based, lacking in-service evaluation platforms. Laser-Induced Breakdown Spectroscopy (LIBS) is emerging as a promising in situ detection technology but is influenced by low air pressure in high-altitude areas. This study investigates how air pressure affects LIBS signals in intumescent coatings on galvanized steel. Using pressures between 35 and 101 kPa, a linear model was developed to correlate laser pulses to ablation depth for characterizing coating thickness. Results show that spectral intensity decreases with lower air pressure. However, a strong linear relationship persists between laser pulses and ablation depth, with a fitting accuracy above 0.9. The coating thickness is identified by the number of laser pulses required to detect the Zn spectral line from the underlying galvanized steel. As air pressure decreases, the ablation depth increases. The study effectively models and corrects for air pressure effects on LIBS data, enabling its application for field detection of fireproof coatings. This advancement enhances the reliability of LIBS technology in assessing the fire performance of these materials, providing a reference for their in situ evaluation and ensuring better fire safety standards for building steel structures and cables.