A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems
N Md Nor, CR Che Hassan… - Reviews in Chemical …, 2020 - degruyter.com
Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …
variations and detect abnormal changes in a process plant. It is always important for early …
Variable selection methods in multivariate statistical process control: A systematic literature review
FAP Peres, FS Fogliatto - Computers & Industrial Engineering, 2018 - Elsevier
Technological advances led to increasingly larger industrial quality-related datasets calling
for process monitoring methods able to handle them. In such context, the application of …
for process monitoring methods able to handle them. In such context, the application of …
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 …
An evaluative study on IoT ecosystem for smart predictive maintenance (IoT-SPM) in manufacturing: Multiview requirements and data quality
With the recent advances of the Internet of Things (IoT), innovative techniques, and concepts
have emerged, such as digital twins and industrial 4.0. As one of the essential parts of a …
have emerged, such as digital twins and industrial 4.0. As one of the essential parts of a …
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 …
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 …
Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring
The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things
(IIoT) have provided manufacturers with the ability to remotely monitor the process by …
(IIoT) have provided manufacturers with the ability to remotely monitor the process by …
Big data approach to batch process monitoring: Simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection
This paper presents a novel data-driven framework for process monitoring in batch
processes, a critical task in industry to attain a safe operability and minimize loss of …
processes, a critical task in industry to attain a safe operability and minimize loss of …
Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method
Large-scale plant-wide processes have become more common and monitoring of such
processes is imperative. This work focuses on establishing a distributed monitoring scheme …
processes is imperative. This work focuses on establishing a distributed monitoring scheme …
New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln
Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in
industrial process monitoring. It intends to promptly detect and identify abnormalities and …
industrial process monitoring. It intends to promptly detect and identify abnormalities and …