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 …
Principal component analysis: A natural approach to data exploration
Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …
areas. This work reports, in an accessible and integrated manner, several theoretical and …
Fault diagnosis based on extremely randomized trees in wireless sensor networks
Abstract Wireless Sensor Network (WSN) being highly diversified cyber–physical system
makes it vulnerable to numerous failures, which can cause devastation towards safety …
makes it vulnerable to numerous failures, which can cause devastation towards safety …
Sensor fault classification based on support vector machine and statistical time-domain features
This paper deals with the problem of fault detection and diagnosis in sensors considering
erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above …
erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above …
Key-performance-indicator-related process monitoring based on improved kernel partial least squares
Y Si, Y Wang, D Zhou - IEEE Transactions on Industrial …, 2020 - ieeexplore.ieee.org
Although the partial least squares approach is an effective fault detection method, some
issues of nonlinear process monitoring related to key performance indicators (KPIs) still …
issues of nonlinear process monitoring related to key performance indicators (KPIs) still …
[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 …
Nonlinear process monitoring using kernel principal component analysis
In this paper, a new nonlinear process monitoring technique based on kernel principal
component analysis (KPCA) is developed. KPCA has emerged in recent years as a …
component analysis (KPCA) is developed. KPCA has emerged in recent years as a …
Multiscale PCA with application to multivariate statistical process monitoring
BR Bakshi - AIChE journal, 1998 - Wiley Online Library
Multiscale principal‐component analysis (MSPCA) combines the ability of PCA to
decorrelate the variables by extracting a linear relationship with that of wavelet analysis to …
decorrelate the variables by extracting a linear relationship with that of wavelet analysis to …