A　transfer　learning-based　deep　convolutional　neural　network　(DCNN)　is　employed　in　this　article　to　identify　several　typical　electrical　substation　equipment　incipient　faults.　Image　dataset　contains　11　000　captured　electrical　substation　equipment　images,　and　each　image　inside　the　dataset　was　labeled　as　normal　condition　or　typical　incipient　faults　(insulation　oil　leakage,　insulator　contamination,　rusting,　and　paint　off).　High-dimension　features　of　electrical　substation　equipment　images　were　first　extracted　by　classical　pre-trained　DCNN　architectures　through　the　transfer　learning　method,　and　different　incipient　faults　were　then　classified　by　the　fully-connected　(FC)　neural　network　using　the　SoftMax　activation　function.　Remarkable　fault　classification　accuracy　was　obtained　on　the　validation　image,　which　verifies　the　effectiveness　of　the　purposed　method.　Performances　of　various　pre-trained　classic　DCNN　architectures　were　explored　and　evaluated　by　t-distributed　stochastic　neighbor　embedding　(t-SNE)　feature　cluster　maps,　learning　curves,　and　confusion　matrixes.　Results　show　that　a　model　consists　of　MobileNetV2　DCNN　architecture　and　two　FC　neural　network　layers　could　finalize　fault　classification　tasks　on　1000　images　in　25　s　with　an　accuracy　of　98%　which　achieves　better　performance　than　traditional　image　classify　methods.　The　proposed　method　could　be　useful　for　designing　an　electrical　substation　image　monitoring　system　thus　providing　early　prediction　of　incipient　equipment　faults.