Sulphur hexafluoride (SF

6

) is the preferred insulating medium for such high-voltage electrical equipment as gas insulated switchgear (GIS) due to its superior electrical properties and chemical stability. In case of such faults as partial discharge inside SF

6

electrical equipment

SF

6

will undergo a decomposition reaction and produce such substances as SO

2

and H

2

S. While

the component content of SF

6

decomposition products has reflected the operating status of the electrical equipment. Therefore

monitoring the gas components inside the equipment can effectively assess the operating status of SF

6

electrical equipment

thus providing an early warning of possible failures inside the equipment. In this paper

a gas-sensitive test platform is set up to study the response characteristics of 12 commercial sensors from Japan and China for H

2

S and SO

2

gases in the SF

6

background. Such four types of sensors as MP-5、TGS2611、TGS2612 and TGS2618

which are best for the choice of two kings of gasses

are selected. On the basis of the experimental data

the SF

6

gas decomposition product data set is constructed to compare the identification accuracy of such five identification algorithms as KNN

support vector machines (SVM)

Naive Bayes

Gradient Boosting and Extra Tree. The results show that extra tree has the highest identification accuracy (83.33%)

and the use of the extra tree algorithm can effectively identify the decomposition products of SF

6

.