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 based fault prognosis for industrial systems: A concise overview
K Zhong, M Han, B Han - IEEE/CAA Journal of Automatica …, 2019 - ieeexplore.ieee.org
Fault prognosis is mainly referred to the estimation of the operating time before a failure
occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial …
occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial …
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
Deep learning networks have been recently utilized for fault detection and diagnosis (FDD)
due to its effectiveness in handling industrial process data, which are often with high …
due to its effectiveness in handling industrial process data, which are often with high …
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 multivariate statistical process monitoring algorithm: Orthonormal subspace analysis
Z Lou, Y Wang, Y Si, S Lu - Automatica, 2022 - Elsevier
Partial least squares (PLS) and canonical correlation analysis (CCA) are two most popular
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
Performance supervised plant-wide process monitoring in industry 4.0: A roadmap
The intensive research and development efforts directed towards large-scale complex
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …
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 …
Robust monitoring and fault isolation of nonlinear industrial processes using denoising autoencoder and elastic net
Robust process monitoring and reliable fault isolation in industrial processes usually
encounter different challenges, including process nonlinearity and noise interference. In this …
encounter different challenges, including process nonlinearity and noise interference. In this …
Parallel PCA–KPCA for nonlinear process monitoring
Q Jiang, X Yan - Control Engineering Practice, 2018 - Elsevier
Both linear and nonlinear relationships may exist among process variables, and monitoring
a process with such complex relationships among variables is imperative. However …
a process with such complex relationships among variables is imperative. However …
Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017
Multivariate statistical process monitoring (MSPM) methods are significant for improving
production efficiency and enhancing safety. However, to the authors' best knowledge, there …
production efficiency and enhancing safety. However, to the authors' best knowledge, there …