In process industries, early detection and diagnosis of faults is crucial for timely identification
of process upsets, equipment and/or sensor malfunctions. Machine learning techniques …

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

For multimode processes, one generally establishes local monitoring models corresponding
to local modes. However, the significant features of previous modes may be catastrophically …

Real-time nonlinear multimode process monitoring of actual industrial systems has attracted
increasing attention recently. In this article, the time-weighed kernel sparse representation …

An unsupervised learning method is developed for fault detection and diagnosis with
missing data for chemical processes based on the multivariate extension of joint recurrence …

Abstract Analysis of industrial data imposes several challenges. These data are acquired
from heterogeneous sources such as sensors, cameras, IoT, etc, and are stored in different …

With the rapid development of modern industry, actual production processes generally have
a variety of complex characteristics, including nonlinearity, multimodality, and contamination …

Process monitoring techniques are used in the chemical industry to improve both product
quality and plant safety. In chemical process systems, real-time process monitoring is one of …

A process plant can have multiple modes of operation due to varying demand, availability of
resources, or the fundamental design of a process. Each of these modes is considered as …

Multi-mode processing is a central feature of modern industry. The application of monitoring
technology to multi-mode processing is crucial to ensure process safety and to enhance …