Intelligent　fault　diagnosis　of　machines　has　yielded　fruitful　achievements;　however,　the　application　in　engineering　scenarios　is　still　not　satisfactory.　There　are　two　reasons:　1)　lacking　fault　signals　of　machines　to　support　the　training　of　diagnosis　models　and　2)　difficulty　in　sensitive　fault　feature　extraction　from　raw　signals.　In　view　of　these,　an　attention-weighted　multidepth　feature　fusion　scheme　with　signals　augmentation　is　proposed.　In　the　proposed　scheme,　generative　adversarial　networks　(GANs)　are　applied　to　expand　the　fault　training　samples　set,　which　helps　improve　the　generalization　ability　of　the　diagnosis　model.　Then,　we　design　a　multidepth　feature　fusion　net　based　on　a　deep　convolutional　neural　network　for　sensitive　feature　learning　from　raw　signals.　Since　features　of　different　depths　may　differ　in　their　ability　to　represent　faults,　the　attention　mechanism　is　utilized　to　adaptively　assign　weights　to　features　of　different　depths　based　on　the　real-time　training　results　of　the　model,　thus　achieving　weighted　fusion　of　multidepth　features.　The　experimental　results　indicate　that　the　proposed　scheme　can　achieve　high　diagnostic　accuracy　with　only　a　small　number　of　fault　training　samples.　Moreover,　the　attention　module　in　the　scheme　is　proved　to　be　effective　in　sensitive　fault　feature　learning　in　two-bearing　diagnosis　cases.