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 …

Those fault detection and diagnosis (FDD) models can identify various faulty signals in
industrial processes by extracting features from process data with high nonlinearity and
correlations. However, the diagnostic performance of those models mainly depends
primarily on the validity of the features extracted from the process data. In this paper, a novel
deep neural network (DNN) model, sparse one-dimensional convolutional neural network
(S1-DCNN), is proposed to learn features from process signals and improve the …