Rapid detection method for the hardness of louver contacts based on laser-induced breakdown spectroscopy (LIBS)

Abstract

The louver contact, a critical component in ultra-high-voltage (UHV) converter transformers, significantly impacts the operational safety of electrical equipment due to its performance stability. The degradation mechanism arises from prolonged exposure to high current, elevated temperatures, and mechanical stress, leading to reduced material hardness and subsequent deterioration of contact performance. In this study, Laser-Induced Breakdown Spectroscopy (LIBS) was employed to characterize aged louver contact specimens. An outlier elimination strategy based on the cosine-distance Local Outlier Factor (LOF) algorithm was implemented. A hybrid dimensionality reduction framework integrating Principal Component Analysis (PCA) with neural encoder architectures was applied to address the dual challenges of high-dimensional data processing and preservation of critical spectral features. This research establishes a LIBS-based surface hardness detection method for louver contacts. Through comparative evaluation of multiple models, the Adam-optimized Gradient Boosting Decision Tree (Adam-GBDT) demonstrated superior performance, achieving a coefficient of determination (R²) of 0.977, and exhibited significant potential for micro-damage surface evaluation. These findings provide technical support for operational monitoring and maintenance strategies for UHV equipment.