Dimension reduction of process dynamic trends using independent component analysis

Li, R.F; Wang, X.Z

doi:10.1016/s0098-1354(01)00773-6

Cited by 86 publications

(39 citation statements)

References 11 publications

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“…21 Thus, ICs reveal more useful information on higher-order statistics from observed data than principal components (PCs). Li and Wang 22 introduced ICA to remove the dependencies of variables and to reduce data dimension of the monitored variables. Kano et al 14 proposed an ICA-based statistical process control (SPC) method and showed its superiority over PCA-based methods.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis based on modified independent component analysis

2006

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Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis based on modified independent component analysis

2006

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“…After p independent components are obtained, we can write the data model of KICA as in Eq. (12) based on the assumption that the observations are generated by the combinations of few independent components (Cardoso and Soulomica, 1993;Back and Weigend, 1997;Cichocki et al, 1998;Cheung and Xu, 1999;Cheung and Xu, 2001;Li and Wang, 2002).…”

Section: Data Model Of Kicamentioning

confidence: 99%

Nonlinear Processes Fault Identification with Application to PCFBP

Zhai

Zhang

et al. 2018

J. Chem. Eng. Japan / JCEJ

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This study focuses on developing a nonlinear fault identi cation approach whose goal is to nd the fault variables after a fault is detected. For nonlinear processes monitoring, it is a challenging problem to identify the fault variables since the monitoring statistics are usually an implicit function with respect to the process variables. In such a case, a traditional contribution plot-based fault identi cation approach that decomposes the monitoring statistics into the summation of variable contributions will be invalid. To solve the issue mentioned above, in this paper, a new nonlinear processes monitoring technique including fault detection and identi cation approaches is proposed based on kernel independent component analysis (KICA). On the basis of KICA, two monitoring statistics are de ned for fault detection, and the contribution of each variable to the monitoring statistics is de ned for fault variable identi cation. After a fault is detected, one can use the proposed fault variable identi cation method to judge which variable has the largest impact on the abnormal event and demonstrate identi cation. At last, the pulverized coal red boiler process (PCFBP) is taken to evaluate the validity and e ectiveness of the proposed approaches. Experiment results show that the proposed methods have satisfactory monitoring performance in the application to PCFBP.

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“…Dimension reduction of ICA is based on the idea that these measured variables are the mixture of some independent variables (Cheung and Xu, 2001;Li and Wang, 2002). An important part of ICA monitoring is the selection of a small number of dominant components from the list of all independent components.…”

Section: Ordering and Dimension Reduction Of Icamentioning

confidence: 99%

On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis.

Lee

2003

J. Chem. Eng. Japan

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In many industries, the effective monitoring and control of batch processes is crucial to the production of high-quality materials. Several techniques using multivariate statistical analysis have been developed for monitoring and fault detection of batch processes. Multiway principal component analysis (MPCA) has shown a powerful monitoring performance in many industrial batch processes. However, it has shortcomings that all batch lengths should be equalized and future values of batches should be estimated for on-line monitoring. In order to overcome these drawbacks and obtain better monitoring performance, we propose a new statistical method for on-line batch process monitoring that uses different unfolding method and independent component analysis (ICA). If the measured data set contains non-Gaussian latent variables, the ICA solution can extract the original source signal to a much greater extent than the PCA solution since ICA involves higher-order statistics and is not based on the assumption that the latent variables follow a multivariate Gaussian distribution. The proposed monitoring method was applied to fault detection and identification in the simulation benchmark of the fed-batch penicillin production, which is characterized by some fault sources with non-Gaussian characteristics. The simulation results clearly show the power and advantages of the proposed method in comparison to MPCA.

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Dimension reduction of process dynamic trends using independent component analysis

Cited by 86 publications

References 11 publications

Fault detection and diagnosis based on modified independent component analysis

Fault detection and diagnosis based on modified independent component analysis

Nonlinear Processes Fault Identification with Application to PCFBP

On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis.

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