Classification of Polluted Silicone Rubber Insulators by Using LIBS Assisted Machine Learning Techniques

Abstract

Silicone rubber (SR) samples are coated with various types of artificially prepared pollutants, in order to identify and distinguish them by employing laser induced breakdown spectroscopy (LIBS). LIBS analysis is successful in identifying the elemental composition of the various types of pollutants. The presence of copper sulphate as well as carbon-based compounds such as fly ash, coal and calcium-based compounds such as cement and calcium phosphate (fertilizer) have been identified by the increment in the normalized intensity ratio of the copper, carbon and calcium peaks respectively. LIBS spectral data has been used in conjunction with several machine learning (ML) algorithms such as linear discriminant analysis, decision tree, K-nearest neighbors and various gradient boosting techniques to classify seven different types of contaminated SR samples. When compared to the other ML approaches utilized in the present study, classification using the Light gradient boosting technique has reflected better classification accuracy of 97.43% with a reasonable computation time of 5.1 s.

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

“…The analysis and classification of polluted silicone rubber insulators using LIBS followed by an assortment of machine learning techniques was described by Sanjana et al 116 Pollutants included copper sulfate, carbon-based contaminants such as coal and fly ash, and Ca-containing materials representing materials such as cement and calcium phosphate fertiliser. Using the data obtained from the analysis of seven different groupings of samples, several machine learning techniques were used and their performance compared.…”

Laser-Induced Breakdown Spectroscopy Analysis of Sheet Molding Compound Materials

“…Energies 2024, 17, 2964 2 of 19 Additionally, they applied LIBS to study surface contamination issues on insulators. By integrating Principal Component Analysis (PCA), k-means clustering, and Partial Least Squares Regression (PLSR), they accomplished quantitative detection of metal contamination on high-voltage transmission line insulators [17].…”

“…This method also distinguished between algal contamination, non-algal contamination, and uncontaminated conditions [18]. Sanjana and colleagues [19] utilized LIBS and machine learning techniques to classify contaminated silicone rubber insulators. Through LIBS analysis and employing various machine learning algorithms for classification, light gradient boosting technology demonstrated the highest classification accuracy of 97.43%.…”

A Review of quantum algorithms for prediction of hazardous asteroids

“…Traditional algorithms, while effective to a certain extent, may face limitations in handling the intricate nature of celestial dynamics. Through the use of quantum computing, the development of quantum machine learning (QML) algorithms presents a paradigm change that could fundamentally alter how we forecast hazardous asteroids [8].…”