Based on the problem of ignoring the slight difference between normal data and fault data and insensitive detection of traditional data-driven process fault detection, this paper proposes a fault detection method based on the combination of FRFT and CNN. Starting from amplifying the small differences between normal data and fault data, a residual matrix is constructed by CVDA for data monitoring to enhance sensitivity. The second is to use FRFT to transform the data, convert some faults with low amplitude and easy to be masked by noise from the time domain to the frequency domain, and amplify their characteristics as much as possible to make them easy to detect. Finally, CNN is used to detect the processed data, which solves the problems of ignoring small differences and low detection sensitivity, and experiments are verified by TE process, which improves the fault detection rate and shows the effectiveness of the proposed method.