A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis
Z Lou, Y Wang, Y Si, S Lu - Automatica, 2022 - Elsevier
Partial least squares (PLS) and canonical correlation analysis (CCA) are two most popular
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems
Abstract The Tennessee Eastman Process (TEP) is a frequently used benchmark in
chemical engineering research. An extended simulator, published in 2015, enables a more …
chemical engineering research. An extended simulator, published in 2015, enables a more …
Improved locality preserving projections based on heat-kernel and cosine weights for fault classification in complex industrial processes
Data-driven fault diagnosis techniques have been widely used in industrial processes.
However, facing a large amount of high-dimensional, nonlinear, and strongly coupled …
However, facing a large amount of high-dimensional, nonlinear, and strongly coupled …
[图书][B] Introduction to process control
JA Romagnoli, A Palazoglu - 2005 - taylorfrancis.com
Improvements in software, instrumentation, and feedback control as well as deepening
linkages between fundamental aspects of process technology have vastly changed the …
linkages between fundamental aspects of process technology have vastly changed the …
Sparse canonical variate analysis approach for process monitoring
Canonical variate analysis (CVA) has shown its superior performance in statistical process
monitoring due to its effectiveness in handling high-dimensional, serially, and cross …
monitoring due to its effectiveness in handling high-dimensional, serially, and cross …
Least squares sparse principal component analysis and parallel coordinates for real-time process monitoring
The unprecedented growth of machine-readable data throughout modern industrial systems
has major repercussions for process monitoring activities. In contrast to model-based …
has major repercussions for process monitoring activities. In contrast to model-based …
An easy to use GUI for simulating big data using Tennessee Eastman process
EB Andersen, IA Udugama, KV Gernaey… - Quality and …, 2022 - Wiley Online Library
Data‐driven process monitoring and control techniques and their application to industrial
chemical processes are gaining popularity due to the current focus on Industry 4.0 …
chemical processes are gaining popularity due to the current focus on Industry 4.0 …
Unsupervised isolation of abnormal process variables using sparse autoencoders
ÁD Hallgrímsson, HH Niemann, M Lind - Journal of Process Control, 2021 - Elsevier
Isolation of abnormal changes in process variables is an integral component of fault
diagnosis, as it provides evidential information for determining the root cause of a detected …
diagnosis, as it provides evidential information for determining the root cause of a detected …
Sparse PCA support exploration of process structures for decentralized fault detection
With the ever-increasing use of sensor technologies in industrial processes and more data
becoming available to engineers, the fault detection and isolation activities in the context of …
becoming available to engineers, the fault detection and isolation activities in the context of …
Experiences with big data: Accounts from a data scientist's perspective
Manufacturing has been rejuvenated by automation and digitalization. This has brought forth
the new industrial era also called Industry 4.0. During the last few years, we have …
the new industrial era also called Industry 4.0. During the last few years, we have …