Fault detection and diagnosis (FDD) have been major concerns in abnormal event management of chemical processes for decades. Frequency-wise variations in chemical processes are not considered in most traditional methods, which affects the monitoring performance. An amplitude-frequency imagesbased convolutional neural network (ConvNet) is proposed for FDD in chemical processes. The fast Fourier transform (FFT) is first performed on data slice collected within a period to extract both amplitude-wise dynamics and frequency-wise variations, with the results in images. Then, the amplitudefrequency images are fed into ConvNet for FDD. ConvNet is applied as a binary classifier, in which each classifier corresponds to only one fault. Thus, an expandable framework is provided to incorporate a new fault. The performance of the proposed amplitude-frequency images-based ConvNet in FDD is demonstrated in a numerical case and the Tennessee Eastman process. KEYWORDS amplitude-frequency image, chemical process, convolutional neural network, fast Fourier transform, fault detection and diagnosis