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 …
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
A data-driven Bayesian network learning method for process fault diagnosis
This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …
Bayesian networks in fault diagnosis
Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and
troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals …
troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals …
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 …
Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges
Abstract Fault Diagnosis and Health Monitoring (FD-HM) for modern control systems have
been an active area of research over the last few years. Model-based FD-HM computational …
been an active area of research over the last few years. Model-based FD-HM computational …
Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data
In order to deal with the modeling and monitoring issue of large-scale industrial processes
with big data, a distributed and parallel designed principal component analysis approach is …
with big data, a distributed and parallel designed principal component analysis approach is …
Large-scale chemical process causal discovery from big data with transformer-based deep learning
X Bi, D Wu, D Xie, H Ye, J Zhao - Process Safety and Environmental …, 2023 - Elsevier
Fault diagnosis is critical for ensuring safe and stable chemical production. Correct
identification of causal relationships among variables in large-scale chemical processes is a …
identification of causal relationships among variables in large-scale chemical processes is a …
Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification
In recent years, machine learning algorithms have been successfully applied to industrial
processes. However, the concurrent analysis of static and dynamic representations has not …
processes. However, the concurrent analysis of static and dynamic representations has not …
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 …