Review on data-driven modeling and monitoring for plant-wide industrial processes
Z Ge - Chemometrics and Intelligent Laboratory Systems, 2017 - Elsevier
Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …
attention in both academy and industry. This paper provides a systematic review on data …
Data-driven control: Overview and perspectives
Process systems are characterized by nonlinearity, uncertainty, large scales, and also the
need of pursuing both safety and economic optimality in operations. As a result they are …
need of pursuing both safety and economic optimality in operations. As a result they are …
An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems
Abstract The Tennessee Eastman Process (TEP) is a frequently used benchmark in
chemical engineering research. An extended simulator, published in 2015, enables a more …
chemical engineering research. An extended simulator, published in 2015, enables a more …
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 …
Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach
In this work, a systematic distributed Bayesian network approach is proposed for modeling
and monitoring large-scale plant-wide processes. First, to deal with the large-scale process …
and monitoring large-scale plant-wide processes. First, to deal with the large-scale process …
Adaptive soft sensors for quality prediction under the framework of Bayesian network
Soft sensor is widely used to predict quality-relevant variables which are usually hard to
measure timely. Due to model degradation, it is necessary to construct an adaptive model to …
measure timely. Due to model degradation, it is necessary to construct an adaptive model to …
Process monitoring using kernel density estimation and Bayesian networking with an industrial case study
R Gonzalez, B Huang, E Lau - ISA transactions, 2015 - Elsevier
Principal component analysis has been widely used in the process industries for the
purpose of monitoring abnormal behaviour. The process of reducing dimension is obtained …
purpose of monitoring abnormal behaviour. The process of reducing dimension is obtained …
Hierarchical Bayesian network modeling framework for large-scale process monitoring and decision making
In this brief, a hierarchical Bayesian network modeling framework is formulated for large-
scale process monitoring and decision making, which includes a basic layer and a …
scale process monitoring and decision making, which includes a basic layer and a …
Root cause diagnosis in multivariate time series based on modified temporal convolution and multi-head self-attention
Y Zhou, K Xu, F He - Journal of Process Control, 2022 - Elsevier
Accurate causal discovery is significant for the data-driven root cause diagnosis. A novel
framework based on modified temporal convolution and multi-head self-attention (MTCMS) …
framework based on modified temporal convolution and multi-head self-attention (MTCMS) …
Data-driven fault diagnosis for chemical processes using transfer entropy and graphical lasso
H Lee, C Kim, S Lim, JM Lee - Computers & Chemical Engineering, 2020 - Elsevier
Process monitoring, especially fault diagnosis, is an indispensable component in terms of
process safety and profit. Since transfer entropy as a data-driven technique for fault …
process safety and profit. Since transfer entropy as a data-driven technique for fault …