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 …
[HTML][HTML] Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era
Safe, efficient, and sustainable operations and control are primary objectives in industrial
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
manufacturing processes. State-of-the-art technologies heavily rely on human intervention …
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 …
Slow-feature-analysis-based batch process monitoring with comprehensive interpretation of operation condition deviation and dynamic anomaly
In order to provide more sensitive monitoring results, the time dynamics and steady-state
operating conditions should be separately monitored by distinguishing time information from …
operating conditions should be separately monitored by distinguishing time information from …
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 …
Dynamic distributed monitoring strategy for large-scale nonstationary processes subject to frequently varying conditions under closed-loop control
C Zhao, H Sun - IEEE Transactions on Industrial Electronics, 2018 - ieeexplore.ieee.org
Large-scale processes under closed-loop control are commonly subjected to frequently
varying conditions due to load changes or other causes, resulting in typical nonstationary …
varying conditions due to load changes or other causes, resulting in typical nonstationary …
Multistep dynamic slow feature analysis for industrial process monitoring
X Ma, Y Si, Z Yuan, Y Qin… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Multivariate statistical process monitoring has been widely used in industry. However,
traditional algorithms often ignore the dynamic characteristics of actual industry process …
traditional algorithms often ignore the dynamic characteristics of actual industry process …
Recursive exponential slow feature analysis for fine-scale adaptive processes monitoring with comprehensive operation status identification
Due to the compensation of the control loops, industrial processes under feedback control
generally reveal typical dynamic behaviors for different operation statuses. Conventional …
generally reveal typical dynamic behaviors for different operation statuses. Conventional …
A generalized probabilistic monitoring model with both random and sequential data
Many multivariate statistical analysis methods and their corresponding probabilistic
counterparts have been adopted to develop process monitoring models in recent decades …
counterparts have been adopted to develop process monitoring models in recent decades …
Recursive slow feature analysis for adaptive monitoring of industrial processes
Recently, a new process monitoring and fault diagnosis method based on slow feature
analysis has been developed, which enables concurrent monitoring of both operating point …
analysis has been developed, which enables concurrent monitoring of both operating point …