Equipment Anomaly Diagnosis Based on DGA and Sparse Support Vector Machine
In order to effectively improve the accuracy and efficiency of equipment anomaly diagnosis based on machine learning,a fault diagnosis model based on sparse support vector machine is proposed.Firstly,the principle of abnormal diagnosis and cha-racteristic gas are analyzed,and the relationship between fault types and characteristic gas is given.Secondly,the data is prepro-cessed from 4 aspects,including cleaning,normalization,balance and division.Then,in order to solve the problem of sparsity of least squares support vector machine,a method is proposed to map data samples to a high-dimensional kernel space,and cluster the mapped data in kernel space distance by spectral clustering algorithm,to realize the data preprocessing of least squares sup-port vector machine,so as to realize its sparseness.Finally,the specific experimental analysis is carried out on a small sample dataset.The results show that,for 9 types of faults,compared with other diagnosis models based on different types of support vector machines,the proposed diagnosis model only needs 11 iterations to obtain the maximum fitness value,and the average diag-nosis accuracy rate is 96.67％,with higher accuracy and efficiency.
Anomaly diagnosisMachine learningLeast square support vector machineAnalysis of dissolved gas in oilRarefaction
万方数据
维普
