Since　the　emergence　of　artificial　intelligence　and　deep　learning　methods,　the　fault　diagnosis　of　bearings　in　rotating　machinery　has　gradually　been　realized,　reducing　the　high　costs　of　bearing　faults.　However,　in　the　actual　work　of　the　equipment,　faults　rarely　occur,　resulting　in　less　fault　data.　Therefore,　it　is　necessary　to　study　small　sample　fault　data.　For　the　case　of　less　fault　data,　the　Maml-Triplet　fault　classification　learning　framework　based　on　the　combination　of　maml　and　the　triplet　neural　network　is　proposed.　In　the　framework　of　Maml-Triplet　fault　classification,　firstly,　an　initial　signal　feature　extractor　is　obtained　using　the　Maml　training　method.　Secondly,　the　feature　vectors　corresponding　to　signal　data　are　obtained　using　depth　distance　measurement　learning　in　the　triplet　neural　network,　and　the　fault　type　is　judged　based　on　the　feature　vectors　of　unknown　signal.　The　results　show　that　the　accuracy　of　the　Maml-Triplet　model　is　2%　higher　than　that　of　the　triplet　model　alone　and　5%　higher　than　that　of　the　Maml-CNN　meta　learning　method.　When　there　are　fewer　data　samples,　the　accuracy　gap　is　more　obvious.　Therefore,　in　the　case　of　less　data,　the　Maml-Triplet　model　has　an　excellent　fault　identification　ability.