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Quality-related fault detection attracted more and more attention in quality control and process monitoring. In recent literature, reconstruction based contributions (RBC) are used for isolating faulty variables which affect product quality. If datasets of known faults are available, fault-specific RBCs are used to identify fault types. Otherwise, variable RBCs are used to isolate faulty variables...
In this paper, a multivariate fault prognosis approach based on statistical process monitoring (SPM) methods and time series prediction for flue gas turbine was proposed. A principal component analysis (PCA) model using sample data under normal state was built. Firstly, fault is detected by squared prediction error (SPE) index, then predicted by SVM model. With development of fault process, the SPE...
In this paper, a multivariate fault prognosis approach based on statistical process monitoring (SPM) methods and time series prediction for turbine machine was proposed. A principal component analysis (PCA) model using sample data under normal state was built. Firstly, fault is detected by squared prediction error (SPE) index, then predicted by AR model. With development of fault process, the SPE...
In order to find an effective way to predict running of mechanical equipment, a new prediction method of mechanical fault based on principal component analysis (PCA) is proposed in this study. The disadvantages of traditional prediction methods and the presence and development in mechanical fault of PCA-based predication method were briefly introduced. Theoretical basis, analysis processes and parameters...
The automatic recognition of human face presents a significant challenge to the pattern recognition research community recently. As a traditional method for face recognition, principal component analysis (PCA) may neglect differentia. Since kernel partial least squares (KPLS) creates orthogonal score vectors by using the existing correlations and keeps most of the variance between different classes,...
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