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In industrial processes, the noise and high dimension of process signals usually affect the performance of those methods in fault detection and diagnosis. A predominant property of a fault diagnosis model is to extract effective features from process signals. Wavelet transform is capable of extracting multiscale information that provides effective fault features in time and frequency domain of process signals. In this paper, a new deep neural network (DNN), multichannel one-dimensional convolutional neural network (MC1-DCNN), is proposed to investigate feature learning from high-dimensional process signals. Wavelet transform is used to extract multiscale components with fault features from process signals. MC1-DCNN is able to learn discriminative time–frequency features from these multiscale process signals. Tennessee Eastman process and fed-batch fermentation penicillin process are adopted to …