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 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 …
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 …
Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis
MS Reis, G Gins - Processes, 2017 - mdpi.com
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
Fault detection and pathway analysis using a dynamic Bayesian network
A dynamic Bayesian network (DBN) based fault detection, root cause diagnosis, and fault
propagation pathway identification scheme is proposed. The proposed methodology …
propagation pathway identification scheme is proposed. The proposed methodology …
Fault detection and diagnosis of nonlinear dynamical processes through correlation dimension and fractal analysis based dynamic kernel PCA
Abstract A novel Dynamic Kernel PCA (DKPCA) method is developed for process monitoring
in nonlinear dynamical systems. Classical DKPCA approaches still exhibit vague linearity …
in nonlinear dynamical systems. Classical DKPCA approaches still exhibit vague linearity …
EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis
M Žvokelj, S Zupan, I Prebil - Journal of Sound and Vibration, 2016 - Elsevier
A novel multivariate and multiscale statistical process monitoring method is proposed with
the aim of detecting incipient failures in large slewing bearings, where subjective influence …
the aim of detecting incipient failures in large slewing bearings, where subjective influence …
Automated feature learning for nonlinear process monitoring–An approach using stacked denoising autoencoder and k-nearest neighbor rule
Z Zhang, T Jiang, S Li, Y Yang - Journal of Process Control, 2018 - Elsevier
Modern industrial processes have become increasingly complicated, consequently, the
nonlinearity of data collected from these systems continues to increase. However, the …
nonlinearity of data collected from these systems continues to increase. However, the …
Online reduced kernel principal component analysis for process monitoring
Kernel principal component analysis (KPCA), which is a nonlinear extension of principal
component analysis (PCA), has gained significant attention as a monitoring method for …
component analysis (PCA), has gained significant attention as a monitoring method for …
Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode …
M Žvokelj, S Zupan, I Prebil - Mechanical systems and signal processing, 2011 - Elsevier
The article presents a novel non-linear multivariate and multiscale statistical process
monitoring and signal denoising method which combines the strengths of the Kernel …
monitoring and signal denoising method which combines the strengths of the Kernel …