The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and
correlated data has paved the way for DL-based fault detection and diagnosis (FDD) …

The focus of this work is on Statistical Process Control (SPC) of a manufacturing process
based on available measurements. Two important applications of SPC in industrial settings …

High-G MEMS accelerometers have been widely used in monitoring natural disasters and
other fields. In order to improve the performance of High-G MEMS accelerometers, a …

Process monitoring has played an increasingly significant role in ensuring safe and efficient
manufacturing operations in process industries over the past several years. Chemical …

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 …

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 capability of deep learning (DL) techniques for dealing with non-linear, dynamic and
correlated data has paved the way for developing DL-based solutions for real-world …

Process monitoring is essential for ensuring that the chemical process system functions
smoothly and consistently. Multivariate statistical process monitoring (MSPM) plays a …

Abstract Principal Component Analysis (PCA) is quite popular for fault detection and
diagnosis in industrial applications. PCA assumes linear relationships among the features …