A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network
(Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial …

Principal Component Analysis (PCA) is a statistical process monitoring technique that has
been widely used in industrial applications. PCA methods for Fault Detection (FD) use data …

The application of a supervised deep convolutional autoencoder was tested against partial
least-squares-discriminant analysis (PLS-DA) for fault detection and diagnosis in a penicillin …

The early detection and root cause identification of abnormal events in industrial processes
is important, to allow for timely corrective actions, ensuring continued economic operation …

Development of chemical process technologies shall be based on the analysis of process
data. In the field of process monitoring the recursive Principal Component Analysis (PCA) is …

A Deep Neural Network (DNN) based algorithm is proposed for the detection and
classification of faults in industrial plants. The proposed algorithm has the ability to classify …

The last decade has seen remarkable advances in speech, image, and language
recognition tools that have been made available to the public through computer and mobile …

Kernel principal component analysis (KPCA) has been found to be one of the promising
methods for nonlinear process monitoring in recent years. It effectively captures the data …

Abstract Five different Fault Detection and Isolation (FDI) approaches are compared using
the same experimental system: an industrial shell and tube Heat Exchanger (HE). The FDI …

This paper presents a first principle model based methodology for simultaneous optimal
tuning of a fault detection algorithm and a feedback controller. The key idea is to calculate …