Application of acoustic emission technology on automation detection of polluted insulator

Artificial contaminations tests proved that there is a complicated nonlinear corresponding relationship between the acoustic emission signals emitted by the polluted insulators and the development of contamination discharge. In order to analyze the acoustic emission signals emitted by polluted insulator discharge effectively and estimate the contamination discharge status on the insulator surface rightly, the least square support vector machine (LS-SVM) is produced, which is a new machine learning tool. By using LS-SVM, the assessment model of insulator surface contamination is built. In the assessment model, the multiple variables come from the acoustic emission signals are chosen as the input variables and then the degree of contamination as the output variable. And the feasibility of model is proved by some data in the laboratory simulation. Simulation results show that the LS-SVM classifiers are capable of learning quite well from the raw data samples while processing good classification and generalization ability. The assessment model can meet the actual need of insulator surface contamination automation monitoring.