In order to generate higher-quality products and increase process efficiency, there has been
a strong push in the processing and manufacturing sectors. This has called for the creation …

In recent years, industrial production has become increasingly automated, with the
widespread application of informational and digital technology. Fault detection and …

This paper proposes a novel fault detection and classification method via deep residual
convolutional neural network (DRCNN). The DRCNN captures the deep process features …

This study proposed a novel fault detection and diagnosis method using continuous wavelet
transform (CWT) and a three-dimensional convolutional neural network (3D-CNN). In …

Deep Learning is one of the fastest growing research topics in process systems engineering
due to the ability of deep learning models to represent and predict non-linear behavior in …

The study on fault detection and diagnosis (FDD) of chemical processes has always been
the top priority of the chemical process safety. In this paper, a fault diagnosis method …

Chemical processes usually exhibit complex, high-dimensional, time-varying, and non-
Gaussian characteristics, and the diagnosis of faults in chemical processes is particularly …

The chemical production process is a special dynamic and complex system. It has the
characteristics of instability and danger, thus making safety management in the production …

Background Convolutional autoencoder (CAE) is an unsupervised feature learning method
and shows excellent performance in multivariate fault diagnosis. However, CAE cannot …

The constant and rapid rise in the field of Industrial Internet of Things has enabled the
manufacturing and process industries to have access to large amounts of process data. This …