Survey on data-driven industrial process monitoring and diagnosis
SJ Qin - Annual reviews in control, 2012 - Elsevier
This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …
fault detection and diagnosis that have been developed over the last two decades. The …
Data-driven soft sensors in the process industry
P Kadlec, B Gabrys, S Strandt - Computers & chemical engineering, 2009 - Elsevier
In the last two decades Soft Sensors established themselves as a valuable alternative to the
traditional means for the acquisition of critical process variables, process monitoring and …
traditional means for the acquisition of critical process variables, process monitoring and …
Review of recent research on data-based process monitoring
Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
Data-driven soft sensor development based on deep learning technique
In industrial process control, some product qualities and key variables are always difficult to
measure online due to technical or economic limitations. As an effective solution, data …
measure online due to technical or economic limitations. As an effective solution, data …
On paradigm of industrial big data analytics: From evolution to revolution
The arrival of the intelligent manufacturing and industrial internet era brings more and more
opportunities and challenges to modern industry. Specifically, the revolution of the …
opportunities and challenges to modern industry. Specifically, the revolution of the …
A full‐condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
Chemical processes are in general subject to time variant conditions because of load
changes, product grade transitions, or other causes, resulting in typical nonstationary …
changes, product grade transitions, or other causes, resulting in typical nonstationary …
Statistical process monitoring as a big data analytics tool for smart manufacturing
With ever-accelerating advancement of information, communication, sensing and
characterization technologies, such as industrial Internet of Things (IoT) and high-throughput …
characterization technologies, such as industrial Internet of Things (IoT) and high-throughput …
[图书][B] Fault detection and diagnosis in industrial systems
Early and accurate fault detection and diagnosis for modern manufacturing processes can
minimise downtime, increase the safety of plant operations, and reduce costs. Such process …
minimise downtime, increase the safety of plant operations, and reduce costs. Such process …
A review of process fault detection and diagnosis: Part III: Process history based methods
V Venkatasubramanian, R Rengaswamy… - Computers & chemical …, 2003 - Elsevier
In this final part, we discuss fault diagnosis methods that are based on historic process
knowledge. We also compare and evaluate the various methodologies reviewed in this …
knowledge. We also compare and evaluate the various methodologies reviewed in this …
Statistical process monitoring: basics and beyond
S Joe Qin - Journal of Chemometrics: A Journal of the …, 2003 - Wiley Online Library
This paper provides an overview and analysis of statistical process monitoring methods for
fault detection, identification and reconstruction. Several fault detection indices in the …
fault detection, identification and reconstruction. Several fault detection indices in the …