A review on data-driven process monitoring methods: Characterization and mining of industrial data
Safe and stable operation plays an important role in the chemical industry. Fault detection
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …
and diagnosis (FDD) make it possible to identify abnormal process deviations early and …
A review of kernel methods for feature extraction in nonlinear process monitoring
Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …
patterns in any data set. In this paper, the applications of kernel methods for feature …
A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis
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 …
Key-performance-indicator-related process monitoring based on improved kernel partial least squares
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 …
Statistical process monitoring of a multiphase flow facility
Industrial needs are evolving fast towards more flexible manufacture schemes. As a
consequence, it is often required to adapt the plant production to the demand, which can be …
consequence, it is often required to adapt the plant production to the demand, which can be …
Nonlinear process fault diagnosis based on serial principal component analysis
Many industrial processes contain both linear and nonlinear parts, and kernel principal
component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the …
component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the …
Independent component analysis application for fault detection in process industries: Literature review and an application case study for fault detection in multiphase …
GLP Palla, AK Pani - Measurement, 2023 - Elsevier
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 …
of process upsets, equipment and/or sensor malfunctions. Machine learning techniques …
Key-performance-indicator-related state monitoring based on kernel canonical correlation analysis
Q Chen, Y Wang - Control Engineering Practice, 2021 - Elsevier
As a multivariate statistical analysis method, canonical correlation analysis (CCA) performs
well for state monitoring of linear processes, but most industrial processes are nonlinear. To …
well for state monitoring of linear processes, but most industrial processes are nonlinear. To …
Intelligent fault diagnosis for chemical processes using deep learning multimodel fusion
Deep learning technology has been widely used in fault diagnosis for chemical processes.
However, most deep learning technologies currently adopted only use a single network …
However, most deep learning technologies currently adopted only use a single network …
Monitoring nonlinear and non-Gaussian processes using Gaussian mixture model-based weighted kernel independent component analysis
A kernel independent component analysis (KICA) is widely regarded as an effective
approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based …
approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based …