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 …
Survey on data-driven industrial process monitoring and diagnosis
SJ Qin - Annual reviews in control, 2012 - Elsevier
This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …
fault detection and diagnosis that have been developed over the last two decades. The …
[PDF][PDF] 动态系统的故障诊断技术
周东华， 胡艳艳 - 自动化学报, 2009 - aas.net.cn
摘要提出了一种全新的分类框架, 将故障诊断方法整体分为两大类, 即定性分析的方法和定量
分析的方法. 对现有的方法在此框架下进行了划分, 并详细介绍了每种方法的基本思想 …
分析的方法. 对现有的方法在此框架下进行了划分, 并详细介绍了每种方法的基本思想 …
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 …
Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes
Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …
received considerable attention in previous decades. Currently, a plant-wide process …
A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
Principal component analysis (PCA) has been widely applied for data modeling and process
monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic …
monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic …
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 …
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 …
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 …
Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference
Multivariate statistical process monitoring involves dimension reduction and latent feature
extraction in large-scale processes and typically incorporates all measured variables …
extraction in large-scale processes and typically incorporates all measured variables …