Extracting fault subspaces for fault identification of a polyester film process

Valle, Sergio; Qin, S. Joe; Piovoso, Michael J.

doi:10.1109/acc.2001.945682

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…Therefore, it is important to quantify the fault effect on the data by suppressing the normal process variations. Qin and Valle et al developed the expressions to calculate the fault directions in the RS, as given in eq . For an illustrated example, the sample vector x can be projected on the RS during fault situation, where x* and Ξ i f represents the fault-free portion and the actual fault in the RS. Here, Ξ i represents the orthonormality in the fault data and ∥ f ∥ represents the actual fault magnitude that can be altered over time as the fault develops.…”

Section: Fault Detection Based On Principal Component Analysis (Pca) ...mentioning

confidence: 99%

Estimation of Disturbance Propagation Path Using Principal Component Analysis (PCA) and Multivariate Granger Causality (MVGC) Techniques

Ahmed

Shin

et al. 2017

Ind. Eng. Chem. Res.

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Process monitoring and fault diagnosis using the multivariate statistical methodologies has been extensively used in the process and product development industries for the last several decades. The fault in one process variable readily affects all the other associated variables, which makes the fault detection process not only more difficult but also time-consuming. In this study, principal component analysis (PCA)based fault amplification algorithm is developed to detect both the root cause of fault and the fault propagation path in the system. The developed algorithm projects the samples on the residual subspace (RS) to determine the disturbance propagation path. Usually, the RS of the fault data is superimposed with the normal process variations, which should be minimized to amplify the fault magnitude. The RS-containing amplified fault is then converted to the covariance matrix, followed by singular value decomposition (SVD) analysis, which, in turn, generates the fault direction matrix corresponding to the largest eigenvalue. The fault variables are then rearranged according to their magnitude of contribution toward a fault, which, in turn, represents the fault propagation path using an absolute descending order function. Moreover, the multivariate Granger causality (MVGC) algorithm is used to analyze the causal relationship among the variables obtained from the developed algorithm. Both the methodologies are tested on the LNG fractionation process train and distillation column operation, where some fault case scenarios are assumed to estimate the fault directions. It is observed that the hierarchy of variables obtained from fault propagation path algorithm are in good agreement with the MVGC algorithm. Therefore, fault amplification methodology can be used in industrial systems for identifying the root cause of fault, as well as the fault propagation path.

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Section: Fault Detection Based On Principal Component Analysis (Pca) ...mentioning

confidence: 99%

Estimation of Disturbance Propagation Path Using Principal Component Analysis (PCA) and Multivariate Granger Causality (MVGC) Techniques

Ahmed

Shin

et al. 2017

Ind. Eng. Chem. Res.

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“…The impact on process variables differs from fault to fault. Thus a vector or subspace can be extracted as the direction of every fault, which makes it possible to identify faults (Valle et al, 2001). A data-driven diagnosis method for shaft furnace roasting processes is proposed in the next section.…”

Section: Description Of Process Faultsmentioning

confidence: 99%

“…The same variables as in the PCA model are used here. Let Valle et al (2001) discussed the relationship between projections of fault direction and faulty data on residual subspace and then provided a method to extract fault direction. We apply SVD on the residual matrix of faulty data T The fault direction matrix can be chosen as…”

Section: Fault Diagnosis Of Shaft Furnace Roasting Processes Based Onmentioning

confidence: 99%

Data-Driven Fault Diagnosis of Shaft Furnace Roasting Processes Using Reconstruction and Reconstruction-Based Contribution Approaches

Liu

Chai

et al. 2014

IFAC Proceedings Volumes

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“…They are much easier to be applied to real processes than model-based and knowledge-based methods, because of the data-based nature. Instead of a causal model or a mechanistic model, they use an empirical correlation model built from normal operating data to monitor the system and isolate faults with the fault direction extracted from historical faulty data [10]. These approaches have found wide applications in fault detection and diagnosis of different industrial processes, including chemicals, polymers and microelectronics.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PCA based fault diagnosis scheme in imperial smelting process

Hu¹,

Chen²,

Gui³

et al. 2013

2013 10th IEEE International Conference on Control and Automation (ICCA)

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In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio(GLR) test and Singular Value Decomposition(SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to eliminate false alarms and isolate faults efficiently.

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Extracting fault subspaces for fault identification of a polyester film process

Cited by 15 publications

References 10 publications

Estimation of Disturbance Propagation Path Using Principal Component Analysis (PCA) and Multivariate Granger Causality (MVGC) Techniques

Estimation of Disturbance Propagation Path Using Principal Component Analysis (PCA) and Multivariate Granger Causality (MVGC) Techniques

Data-Driven Fault Diagnosis of Shaft Furnace Roasting Processes Using Reconstruction and Reconstruction-Based Contribution Approaches

Adaptive PCA based fault diagnosis scheme in imperial smelting process

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