For the problems of strong noise and low quality abnormal monitoring efficiency in dynamic process of intelligent manufacturing scene, the real-time abnormal quality monitoring method is proposed by combining the variational mode decomposition(VMD) and deep convolutional neural network(CNN). Firstly, the VMD is used to divide the original data into two kinds of intrinsic model functions which reflect the quality abnormal characteristics and noise data. By removing the noise intrinsic model function, strong noise is disposed in the dynamic production process. Furthermore, the intrinsic model functions that retain the original quality abnormal features are transformed into quality abnormal images by gray level transformation, and the VMD-CNN model is constructed to identify the images to monitor dynamic abnormal quality process. Finally, the simulation experiments are used to compare and analysis the CNN, wavelet denoising reconstruction and the proposed method. Results show that the performance of the proposed method is significantly better than the existing dynamic process abnormal quality monitoring methods.