An analysis of process fault diagnosis methods from safety perspectives
Industry 4.0 provides substantial opportunities to ensure a safer environment through online
monitoring, early detection of faults, and preventing the faults to failures transitions. Decision …
monitoring, early detection of faults, and preventing the faults to failures transitions. Decision …
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 …
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 …
Deep convolutional neural network model based chemical process fault diagnosis
H Wu, J Zhao - Computers & chemical engineering, 2018 - Elsevier
Numerous accidents in chemical processes have caused emergency shutdowns, property
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …
A deep belief network based fault diagnosis model for complex chemical processes
Z Zhang, J Zhao - Computers & chemical engineering, 2017 - Elsevier
Data-driven methods have been regarded as desirable methods for fault detection and
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …
Nonlinear principal component analysis using autoassociative neural networks
MA Kramer - AIChE journal, 1991 - Wiley Online Library
Nonlinear principal component analysis is a novel technique for multivariate data analysis,
similar to the well‐known method of principal component analysis. NLPCA, like PCA, is used …
similar to the well‐known method of principal component analysis. NLPCA, like PCA, is used …
Monitoring batch processes using multiway principal component analysis
P Nomikos, JF MacGregor - AIChE Journal, 1994 - Wiley Online Library
Multivariate statistical procedures for monitoring the progress of batch processes are
developed. The only information needed to exploit the procedures is a historical database of …
developed. The only information needed to exploit the procedures is a historical database of …
[HTML][HTML] Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …
impact on chemical engineering. But classical machine learning approaches may be weak …
[图书][B] Model-based fault diagnosis techniques
MODEL-BASED FAULT DIAGNOSIS TECHNIQUES Page 1 CHAPTER2 MODEL-BASED FAULT
DIAGNOSIS TECHNIQUES 2.1 Introduction The model-based approach to fault detection in …
DIAGNOSIS TECHNIQUES 2.1 Introduction The model-based approach to fault detection in …
Fault detection and diagnosis based on transfer learning for multimode chemical processes
H Wu, J Zhao - Computers & Chemical Engineering, 2020 - Elsevier
Fault detection and diagnosis (FDD) has been an active research field during the past
several decades. Methods based on deep neural networks have made some important …
several decades. Methods based on deep neural networks have made some important …