Recent trends on hybrid modeling for Industry 4.0
The chemical processing industry has relied on modeling techniques for process monitoring,
control, diagnosis, optimization, and design, especially since the third industrial revolution …
control, diagnosis, optimization, and design, especially since the third industrial revolution …
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 …
The promise of artificial intelligence in chemical engineering: Is it here, finally?
V Venkatasubramanian - AIChE Journal, 2019 - search.ebscohost.com
The article discusses the presence and potential of Artificial Intelligence in Chemical
Engineering and discusses its background. Topics include the Phases of Artificial …
Engineering and discusses its background. Topics include the Phases of Artificial …
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 …
[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 …
Risk-based fault detection and diagnosis for nonlinear and non-Gaussian process systems using R-vine copula
This paper presents a risk-based fault detection and diagnosis methodology for nonlinear
and non-Gaussian process systems using the R-vine copula and the event tree. The R-vine …
and non-Gaussian process systems using the R-vine copula and the event tree. The R-vine …
A novel data‐driven methodology for fault detection and dynamic risk assessment
This paper presents a novel methodology for dynamic risk analysis, integrating the
multivariate data‐based process monitoring and logical dynamic failure prediction model …
multivariate data‐based process monitoring and logical dynamic failure prediction model …
[图书][B] Statistical monitoring of complex multivatiate processes: with applications in industrial process control
The development and application of multivariate statistical techniques in process monitoring
has gained substantial interest over the past two decades in academia and industry alike …
has gained substantial interest over the past two decades in academia and industry alike …
An interpretable unsupervised Bayesian network model for fault detection and diagnosis
Process monitoring is a critical activity in manufacturing industries. A wide variety of data-
driven approaches have been developed and employed for fault detection and fault …
driven approaches have been developed and employed for fault detection and fault …
Multiscale principal component analysis-signed directed graph based process monitoring and fault diagnosis
The chemical process industry has become the backbone of the global economy. The
complexities of chemical process systems have been increased in the last two decades due …
complexities of chemical process systems have been increased in the last two decades due …