Dissolved Gas Analysis (DGA) data of liquid insulation used to find the incipient faults such as partial discharge, thermal faults of various temperatures, discharge of high and low energy faults, combination of electrical and thermal faults in transformers. The conventional approaches of DGA namely Gas Ratio method, Duval triangle method and the Neural Network seems to be time consuming and sometimes yield erroneous results. In this paper, Adaptive BOOST machine learning algorithm is proposed, which is effective in classifying the transformer incipient faults. The results of proposed algorithm is compared with the results of different other machine learning algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Ensembler algorithm for the same set of transformers data. From the comparison, it is evident that ADABOOST machine learning algorithm performs well.

液体绝缘的溶解气体分析（DGA）数据，用于查找初期故障，例如局部放电，各种温度的热故障，高能和低能故障的放电，变压器的电气和热故障的组合。DGA的常规方法，即气体比率法，Duval三角法和神经网络，似乎很耗时，有时会产生错误的结果。本文提出了一种自适应BOOST机器学习算法，该算法对变压器的早期故障分类是有效的。将该算法的结果与同一套变压器数据的不同其他机器学习算法（例如K最近邻（KNN），支持向量机（SVM），决策树，集成器算法）的结果进行了比较。从比较中