107 related articles for article (PubMed ID: 31078289)
1. Statistical monitoring for non-Gaussian processes based on MICA-KDR method.
Lan T; Tong C; Yu H; Shi X
ISA Trans; 2019 Nov; 94():164-173. PubMed ID: 31078289
[TBL] [Abstract][Full Text] [Related]
2. A missing variable approach for decentralized statistical process monitoring.
Tong C; Lan T; Zhu Y; Shi X; Chen Y
ISA Trans; 2018 Oct; 81():8-17. PubMed ID: 30262178
[TBL] [Abstract][Full Text] [Related]
3. Double-layer ensemble monitoring of non-gaussian processes using modified independent component analysis.
Tong C; Lan T; Shi X
ISA Trans; 2017 May; 68():181-188. PubMed ID: 28193441
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.
Mansouri M; Nounou MN; Nounou HN
IEEE Trans Nanobioscience; 2017 Sep; 16(6):504-512. PubMed ID: 28708564
[TBL] [Abstract][Full Text] [Related]
6. Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.
Gopinath K; Krishnamurthy V; Lacey S; Sathian K
Brain Connect; 2018 Feb; 8(1):10-21. PubMed ID: 29161884
[TBL] [Abstract][Full Text] [Related]
7. Application of multiway ICA for on-line process monitoring of a sequencing batch reactor.
Yoo CK; Lee DS; Vanrolleghem PA
Water Res; 2004 Apr; 38(7):1715-32. PubMed ID: 15026226
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Fault Detection and Isolation of Non-Gaussian and Nonlinear Processes Based on Statistics Pattern Analysis and the
Zhou Z; Wang J; Yang C; Wen C; Li Z
ACS Omega; 2022 Jun; 7(22):18623-18637. PubMed ID: 35694521
[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. Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes.
Wang S; Wang Y; Tong J; Chang Y
Sensors (Basel); 2023 Jan; 23(2):. PubMed ID: 36679784
[TBL] [Abstract][Full Text] [Related]
12. Adaptive process monitoring via online dictionary learning and its industrial application.
Huang K; Wu Y; Long C; Ji H; Sun B; Chen X; Yang C
ISA Trans; 2021 Aug; 114():399-412. PubMed ID: 33397583
[TBL] [Abstract][Full Text] [Related]
13. Industrial Process Monitoring Based on Dynamic Overcomplete Broad Learning Network.
Peng C; Ying X; ZhiQi H
IEEE Trans Neural Netw Learn Syst; 2024 Feb; 35(2):1761-1772. PubMed ID: 35802548
[TBL] [Abstract][Full Text] [Related]
14. Fault detection for NOx emission process in thermal power plants using SIP-PCA.
Ren M; Liang Y; Chen J; Xu X; Cheng L
ISA Trans; 2023 Sep; 140():46-54. PubMed ID: 37391290
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A Unifying Objective Function of Independent Component Analysis for Ordering Sources by Non-Gaussianity.
Matsuda Y; Yamaguchi K
IEEE Trans Neural Netw Learn Syst; 2018 Nov; 29(11):5630-5642. PubMed ID: 29993873
[TBL] [Abstract][Full Text] [Related]
17. Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis.
Tong C; Shi X; Lan T
ISA Trans; 2016 Nov; 65():407-417. PubMed ID: 27435000
[TBL] [Abstract][Full Text] [Related]
18. A combined mono- and multi-turbine approach for fault indicator synthesis and wind turbine monitoring using SCADA data.
Lebranchu A; Charbonnier S; Bérenguer C; Prévost F
ISA Trans; 2019 Apr; 87():272-281. PubMed ID: 30545768
[TBL] [Abstract][Full Text] [Related]
19. Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals.
Barbati G; Porcaro C; Zappasodi F; Rossini PM; Tecchio F
Clin Neurophysiol; 2004 May; 115(5):1220-32. PubMed ID: 15066548
[TBL] [Abstract][Full Text] [Related]
20. An improved incipient fault detection method based on Kullback-Leibler divergence.
Chen H; Jiang B; Lu N
ISA Trans; 2018 Aug; 79():127-136. PubMed ID: 29801923
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]