128 related articles for article (PubMed ID: 32854955)
1. Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring.
Zhang MQ; Luo XL
ISA Trans; 2021 Feb; 108():106-120. PubMed ID: 32854955
[TBL] [Abstract][Full Text] [Related]
2. Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference.
Wang X; Wu P
ACS Omega; 2022 Jun; 7(22):18904-18921. PubMed ID: 35694473
[TBL] [Abstract][Full Text] [Related]
3. A nonlinear quality-related fault detection approach based on modified kernel partial least squares.
Jiao J; Zhao N; Wang G; Yin S
ISA Trans; 2017 Jan; 66():275-283. PubMed ID: 27817839
[TBL] [Abstract][Full Text] [Related]
4. Quality-Relevant Process Monitoring with Concurrent Locality-Preserving Dynamic Latent Variable Method.
Zhang Q; Lu S; Xie L; Chen Q; Su H
ACS Omega; 2022 Aug; 7(31):27249-27262. PubMed ID: 35967037
[TBL] [Abstract][Full Text] [Related]
5. Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis.
Cai L; Tian X; Chen S
IEEE Trans Neural Netw Learn Syst; 2017 Jan; 28(1):122-135. PubMed ID: 26685274
[TBL] [Abstract][Full Text] [Related]
6. Quality monitoring method based on enhanced canonical component analysis.
Yang J; Dong J; Shi H; Tan S
ISA Trans; 2020 Oct; 105():221-229. PubMed ID: 32624172
[TBL] [Abstract][Full Text] [Related]
7. Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring.
Deng X; Wang L
ISA Trans; 2018 Jan; 72():218-228. PubMed ID: 29017769
[TBL] [Abstract][Full Text] [Related]
8. Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.
Zhang H; Tian X; Deng X; Cao Y
ISA Trans; 2018 Aug; 79():108-126. PubMed ID: 29776590
[TBL] [Abstract][Full Text] [Related]
9. Kernel-based PMP structure for nonlinear industrial quality-related process monitoring.
Ma H; Wang Y; Chen H; Yuan J; Ji Z
ISA Trans; 2023 Oct; 141():184-196. PubMed ID: 37474433
[TBL] [Abstract][Full Text] [Related]
10. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.
Deng X; Tian X; Chen S; Harris CJ
IEEE Trans Neural Netw Learn Syst; 2018 Mar; 29(3):560-572. PubMed ID: 28026785
[TBL] [Abstract][Full Text] [Related]
11. KPLS-KSER based approach for quality-related monitoring of nonlinear process.
Jiao J; Zhen W; Wang G; Wang Y
ISA Trans; 2021 Feb; 108():144-153. PubMed ID: 32981684
[TBL] [Abstract][Full Text] [Related]
12. Complex dynamic process monitoring method based on slow feature analysis model of multi-subspace partitioning.
Li Z; Yan X
ISA Trans; 2019 Dec; 95():68-81. PubMed ID: 31151751
[TBL] [Abstract][Full Text] [Related]
13. Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data.
Yoshida K; Yoshimoto J; Doya K
BMC Bioinformatics; 2017 Feb; 18(1):108. PubMed ID: 28196464
[TBL] [Abstract][Full Text] [Related]
14. Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring.
Jaffel I; Taouali O; Harkat MF; Messaoud H
ISA Trans; 2016 Sep; 64():184-192. PubMed ID: 27342996
[TBL] [Abstract][Full Text] [Related]
15. Efficient Iterative Dynamic Kernel Principal Component Analysis Monitoring Method for the Batch Process with Super-large-scale Data Sets.
Wang Y; Yu H; Li X
ACS Omega; 2021 Apr; 6(15):9989-9997. PubMed ID: 34056154
[TBL] [Abstract][Full Text] [Related]
16. Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis.
Li C; Zhou Z; Wen C; Li Z
ACS Omega; 2022 Mar; 7(8):6978-6990. PubMed ID: 35252689
[TBL] [Abstract][Full Text] [Related]
17. LSTMED: An uneven dynamic process monitoring method based on LSTM and Autoencoder neural network.
Deng W; Li Y; Huang K; Wu D; Yang C; Gui W
Neural Netw; 2023 Jan; 158():30-41. PubMed ID: 36442372
[TBL] [Abstract][Full Text] [Related]
18. Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation.
Wang J; Zhao S; Wang E; Zhao J; Liu X; Li Z
Sensors (Basel); 2023 Sep; 23(19):. PubMed ID: 37836926
[TBL] [Abstract][Full Text] [Related]
19. Novel dynamic enhanced robust principal subspace discriminant analysis for high-dimensional process fault diagnosis with industrial applications.
Zhang MQ; Luo XL
ISA Trans; 2021 Aug; 114():1-14. PubMed ID: 33388145
[TBL] [Abstract][Full Text] [Related]
20. Decomposition of nonlinear non-Gaussian process and its application to nonlinear filter and predictor design.
Shi J; Sun HH
Ann Biomed Eng; 1991; 19(4):457-72. PubMed ID: 1741526
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]