Machine　learning　algorithm　based　on　hand-crafted　features　from　the　raw　vibration　signal　has　shown　promising　results　in　the　hydroelectric　generating　unit　(HGU)　fault　diagnosis　in　recent　years.　Such　methodologies,　nevertheless,　can　lead　to　important　information　loss　in　representing　the　vibration　signal,　which　intrinsically　relies　on　engineering　experience　of　diagnostic　experts　and　prior　knowledge　about　feature　extraction　techniques.　Therefore,　in　this　paper,　an　effective　and　stable　HGU　fault　diagnosis　system　using　one-dimensional　convolutional　neural　network　(1-D　CNN)　and　gated　recurrent　unit　(GRU)　based　on　the　sequence　data　structure　is　proposed.　First,　the　raw　vibration　data　is　reconstructed　by　data　segmentation,　which　can　improve　training　efficiency.　Second,　the　reconstruction　data　under　the　influence　of　different　running　conditions　and　various　fault　factors　can　be　effectively　and　adaptively　learned　by　1-D　CNN-GRU　and　then　determine　information　fault　categories　via　network　inference.　Finally,　four　machine　learning　methods　are　applied　to　diagnosis　the　reconstruction　data　based　on　the　experimental　dataset.　The　performance　of　the　proposed　method　is　verified　by　comparing　with　the　results　of　other　machine　learning　techniques.　Furthermore,　the　fault　diagnostic　model,　which　is　trained　by　the　practical　vibration　signal,　has　successfully　applied　in　engineering　practice.　©　2001-2012　IEEE.