Open AccessClassification of arrester defects based on Naive Bayesian inference
Author(s) -
Ning Ding,
Ming Yang,
Ning Su,
Shiyong Qi,
Qingming Lu
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/804/4/042006
Subject(s) - lightning arrester , correctness , reliability engineering , lightning (connector) , bayesian probability , computer science , surge arrester , bayesian inference , naive bayes classifier , engineering , data mining , artificial intelligence , electrical engineering , algorithm , power (physics) , physics , quantum mechanics , support vector machine
Under the special environment of high temperature, high humidity and high salt, the development of the deterioration or latent defects of the arrester is accelerated. It is difficult to identify the abnormal state of arrester in special environment, only relying on the monitoring index of arrester. Therefore, this paper proposes a kind of lightning arrester defect recognition technology based on Naive Bayesian, which extracts the key features that affect the operation state of lightning arrester in special environment, and calculates the prior probability of training samples and the posterior probability of test samples through naive Bayesian algorithm, so as to identify the type of lightning arrester defects. The feasibility and correctness of the proposed method are analyzed and verified by the actual monitoring and detection data.