To overcome the influence of background noise interference on vibration detection efficiency,an anti-interference framework based on generation adversarial networks and convolutional neural networks (CNNs) is proposed to realize the contact defect detection for field gas insulated switchgear (GIS). Firstly,by current-carrying tests on prototype GIS platform,vibration waveforms of GIS with four artificial designed contact defects (missing finger,loosening bolt,with decomposed products and insufficient conductor insert depth) are acquired. Vibration waveforms on field GIS which contain background noise interference are also collected as a reference. Dataset for background noise interference removal and contact fault classification is built through spectrogram transform. Secondly,a cycle-consistent generative adversarial network (CycleGAN) with field vibration spectrogram as input is adopted to remove background noise interference on GIS. Then,two classical CNN architectures (AlexNet,ResNet18) are empirically designed to extract defeat features hidden in vibration spectrograms. Finally,the contact faults are identified via fully connected classifier. Influence of different time-frequency transformation algorithms on fault classification results are also compared. The results show that the proposed model can obtain maximum mean discrepancy (MMD) with 0.956 0 and Fréchet inception distance (FID) with 62.09 on field dataset,and the Mel-ResNet18 model could obtain 99.43% contact defect classification accuracy on test dataset. The proposed method in this paper can bring sound application value on improving the effectiveness of vibration detection and contact defect diagnosis results of field GIS.