2020
DOI: 10.1002/cjce.23832
|View full text |Cite
|
Sign up to set email alerts
|

Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis

Abstract: The batch process generally covers high nonlinearity and two-directional dynamics: time-wise dynamics, which correspond to inherently time-varying dynamics resulting from the slowly varying underlying driving forces within each batch duration; and batch-wise dynamics, which are associated with different operating modes among different batches. However, most existing dynamic nonlinear monitoring methods cannot extract the slowly varying underlying driving forces of the nonlinear batch process and rarely tackle … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
14
0

Year Published

2021
2021
2022
2022

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 19 publications
(14 citation statements)
references
References 63 publications
0
14
0
Order By: Relevance
“…Although KSFA has been incorporated into several recent works on process monitoring (Zhang et al, 2017) and process fault detection (Zhang et al, 2021;Zhang et al, 2018;Zhang et al, 2015), the effect of the nonlinear slow features from KSFA as inputs to a nonlinear regression model has not yet been investigated. Soft-sensor design differs from process monitoring and other process applications by using regression/machine learning-type methods to create predictions of difficult to measure key product quality variables through other easy to measure process variables.…”
Section: Soft-sensing Using Kernel Slow Feature Analysis and Neural N...mentioning
confidence: 99%
“…Although KSFA has been incorporated into several recent works on process monitoring (Zhang et al, 2017) and process fault detection (Zhang et al, 2021;Zhang et al, 2018;Zhang et al, 2015), the effect of the nonlinear slow features from KSFA as inputs to a nonlinear regression model has not yet been investigated. Soft-sensor design differs from process monitoring and other process applications by using regression/machine learning-type methods to create predictions of difficult to measure key product quality variables through other easy to measure process variables.…”
Section: Soft-sensing Using Kernel Slow Feature Analysis and Neural N...mentioning
confidence: 99%
“…5. WNM x i , x j À Á of process variables in X G and X N can be computed using Equations ( 4), ( 5), and (9). NM m N and NM m G of X G and X N can be computed using Equations ( 10) and (11).…”
Section: Comprehensive Process Monitoringmentioning
confidence: 99%
“…[1][2][3][4][5] Process monitoring is an effective means to detect abnormal working conditions and improve products quality. [6][7][8][9][10] The existing methods are usually divided into the model-based and data-based methods. [11,12] The model-based methods are generally difficult to be applied due to the complexity of industrial processes and incomplete prior process knowledge.…”
Section: Introductionmentioning
confidence: 99%
“…[ 1 ] Hence, establishing an effective monitoring system for trustworthy fault detection and diagnosis is important to ensure the safety of batch production processes, which has drawn increasing attention from many researchers and practitioners. [ 2–4 ]…”
Section: Introductionmentioning
confidence: 99%