Industrial Internet of Things (IIoT) systems connect a plethora of smart devices, such as
sensors, actuators, and controllers, to enable efficient industrial productions in manners …

The present paper brings together openly available datasets and simulators for testing of
process monitoring and fault diagnosis techniques. Some general characteristics of these …

In recent years, there have been studies focusing on the use of different types of
autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and …

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high
dimensional process data has enabled data-driven RNN-based fault detection algorithms …

The proliferation of automated data collection schemes and the advances in sensorics are
increasing the amount of data we are able to monitor in real-time. However, given the high …

Unsupervised data-driven methods are widely used for fault detection and diagnosis in
modern industrial processes. However, accurately distinguishing faults from normal …

This study compares four models for industrial condition monitoring including a principal
components analysis (PCA) approach and three deep learning models, one of which is a …

Quality Variables of industrial processes generally require to be obtained as fast as
possible. In this paper, a correlation-wise self-organizing fuzzy neural network (CwSFNN) for …

Fault or anomaly detection is one of the key problems faced by the chemical process
industry for achieving safe and reliable operation. In this study, a novel methodology …

Fault detection and diagnosis (FDD) is a promising technology for safe operation, quality
control, and profitability improvement in chemical process systems. In practice, chemical …