Limited　operating　data　resulting　from　complex　and　changeable　working　conditions　significantly　undermines　the　performance　of　deep　learning-based　methods　for　rolling　bearing　fault　diagnosis.　Generally,　this　problem　can　be　solved　by　using　the　generative　adversarial　network　(GAN)　to　augment　data.　Most　GAN-based　methods,　however,　seldom　comprehensively　consider　the　global　interactions　and　local　dependencies　in　raw　vibration　signals　during　data　generation,　leading　to　a　decline　in　the　quality　of　the　generated　data　and　compromising　the　diagnostic　accuracy.　To　address　this　problem,　a　fault　diagnosis　method　based　on　integrated　convolutional　transformer　GAN　(ICoT-GAN)　is　proposed　to　improve　the　diagnostic　performance　under　limited　data　conditions　by　generating　high-quality　signals.　First,　a　new　data　augmentation　model　ICoT-GAN　is　developed.　In　ICoT-GAN,　a　novel　ICoT　block　is　designed　to　construct　the　generator　and　discriminator.　The　ICoT　block　achieves　the　integration　of　attention-based　global　information　capture　and　convolution-based　local　feature　extraction　through　the　incorporation　of　convolution　within　the　transformer　encoder.　This　design　allows　the　ICoT-GAN　to　comprehensively　extract　both　global　and　local　time-series　features　of　raw　signals　and　generate　high-quality　signals.　Second,　a　novel　data　evaluation　indicator,　referred　to　as　the　multiple　time-domain　features　indicator　(MTFI),　is　designed　to　quantitatively　evaluate　generated　signals＇　quality　by　calculating　the　similarity　of　time-domain　features　between　the　real　and　generated　signals.　The　MTFI　can　complement　probability　distribution　indicators　and　provide　a　comprehensive　evaluation　of　the　data　augmentation　model＇s　generation　ability　by　considering　both　time-series　feature　similarity　and　probability　distribution　differences.　Finally,　the　effectiveness　of　our　proposed　method　has　been　successfully　demonstrated　under　limited　data　conditions　using　the　Case　Western　Reserve　University　(CWRU)　and　Center　for　Intelligent　Maintenance　System　(IMS)　bearing　datasets.　With　only　16　training　samples　per　class,　our　proposed　method　achieves　diagnostic　accuracy　of　99.99%　and　99.70%,　respectively.　Additionally,　the　data　generation　time　is　only　1163.31　s,　indicating　the　efficiency　of　our　proposed　method.