2022
DOI: 10.1021/acsomega.1c06649
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Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis

Abstract: Independent component analysis (ICA) is an excellent latent variables (LVs) extraction method that can maximize the non-Gaussianity between LVs to extract statistically independent latent variables and which has been widely used in multivariate statistical process monitoring (MSPM). The underlying assumption of ICA is that the observation data are composed of linear combinations of LVs that are statistically independent. However, the assumption is invalid because the observation data are always derived from th… Show more

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Cited by 5 publications
(3 citation statements)
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“…Li et al proposed monitoring non-Gaussian and nonlinear processes using the independent slow feature analysis (ISFA) that combines statistical independence and temporal slowness in extracting the latent variables. [3] Li and Yan developed a new ensemble monitoring method utilizing the known fault information and multi-objective optimization, that is, a fault-relevant optimal ensemble ICA model for non-Gaussian process monitoring. [4] GMM shows great capability in non-Gaussian process monitoring.…”
Section: Introductionmentioning
confidence: 99%
“…Li et al proposed monitoring non-Gaussian and nonlinear processes using the independent slow feature analysis (ISFA) that combines statistical independence and temporal slowness in extracting the latent variables. [3] Li and Yan developed a new ensemble monitoring method utilizing the known fault information and multi-objective optimization, that is, a fault-relevant optimal ensemble ICA model for non-Gaussian process monitoring. [4] GMM shows great capability in non-Gaussian process monitoring.…”
Section: Introductionmentioning
confidence: 99%
“…The underlying hypothesis is that slow variations are more relevant and informative compared to fast variations. SFA has gained significant popularity in the recent literature on process monitoring [265][266][267][268][269][270][271][272][273][274][275][276][277][278][279][280][281][282].…”
mentioning
confidence: 99%
“…The second important contribution is the adoption of Independent ICA as a more effective approach for capturing non-Gaussian features in wind turbine data. By leveraging ICA's capability to represent original data in latent variables that are both non-Gaussian and independent, the proposed approach surpasses the performance of other semi-supervised schemes, including traditional methods like PCA [37,38]. Furthermore, to address the dynamic nature of wind turbine variables, the authors incorporated Dynamic ICA (DICA), which considers temporal dependencies and past information during the modeling stage.…”
mentioning
confidence: 99%