Research on Development and Application of Support Vector Machine - Transformer Fault Diagnosis

Abstract

Support Vector Machine (SVM) is a machine learning method based on statistical learning theory, solving the problems of classification and regression by means of optimization methods. The method can effectively solve the problem of small number of samples, nonlinearity and high dimensionality, and largely avoids the problems of "dimensionality disaster", "over-fitting" and local minimum caused by traditional statistical theory. However, there are still some problems, such as high complexity of the algorithm and difficulty in adapting to large-scale data. The article systematically introduces the theory of support vector machine, summarizes the common training algorithms of standard (traditional) support vector machine and their existing problems, the new learning models and algorithms developed on this basis. And verify the actual effect and scope of each support vector machine model through the application of transformer fault diagnosis.