Acquisition and analysis of hyperspectral data for surface contamination level of insulating materials

Severe contamination increases the risk of creeping discharge even flashover of the insulating materials. In pursuit of a reliable remote monitoring method to prevent unexpected flashover of contaminated insulating materials, this paper attempts to use hyperspectral imaging technology (HSI) with a targeted bands-based model to evaluate the contamination level. To validate such method, artificial samples with different contamination levels were employed as objects. By using Canny operator, pollution areas were selected as regions of interest. Then, multivariate scattering correction (MSC) was adopted in spectral data preprocessing and its effect was evaluated by principal component analysis (PCA) in terms of the data separability. By means of successive projection algorithm (SPA), six targeted bands were finally determined. In pursuit of a high discriminability of image expression, linear discriminate analysis (LDA) was selected and implemented on the targeted bands-based results. It demonstrated that MSC-SPA-LDA model is of a high discrimination of contamination level as well as a high calculation efficiency, which is significative on the rapid remote diagnosis based on HSI.