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

Data-driven soft sensing modeling plays an increasingly important role in the prediction of
key variables in the process industry. Since data is an essential part of modeling, how to …

It has become a big challenge and a hot topic of research to capture the most relevant
features from high-dimensional process data for enhancing fault diagnosis. To effectively …

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for
assessing the effectiveness of fault detection and false alarm tracking methods in intricate …

In the field of the fault diagnosis of industrial processes, there are many problems in process
data, such as missing critical fault data, high repeatability of normal state data, and poor …

In large-scale industrial processes, fault diagnosis is of paramount importance, as faults
jeopardize the stability and performance of processes. However, effective fault diagnosis …

It has become a challenge to identify the discriminant information and the local geometric
feature from the complex process data for improving fault diagnosis accuracy. Facing this …

With the rapid expansion of the scale of modern industrial processes, more and more
machine learning approaches using process variables for process monitoring and alarm …

Industrial fault diagnosis methods based on graph convolution network (GCN) becomes a
hot topic for its great feature extraction ability to multivariate time-series data. However …

Modern industries are facing diverse market demands and exhibit typical multimode
characteristics. In this context, not only common process characteristics such as nonlinearity …