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Aiming at the multimode industrial process monitoring problem, this paper proposes a fault detection method based on local entropy principal component analysis (LEPCA) algorithm. Firstly, in order to deal with the multimode characteristic of operating data, k nearest neighbor Parzen window (kNN-Parzen) method is used to estimate each sample's local probability density. Then, a local relative density...
Multivariate statistical process control (MSPC) has been widely used for process monitoring. When a fault is detected, it is important to identify an actual cause of the fault. Fault identification methods are classified into two groups by availability of historical data sets obtained from faulty situations. When such historical data sets are not available, contributions from process variables to...
The traditional principal component analysis (PCA) method divides the variable space into two parts: Principal subspace and Residual subspace by orthogonal decomposition. It has been widely used in fault detection process, but it is difficult to interpret the modes of the fault because of model compound effect, and the ability to distinguish the pattern which is no significant is affected. In industrial...
In the MIMO system, the benchmark of the multivariable system in the past is computed from the traces of the multivariable covariance matrices. This may cause false detection since the correlation among variables is not considered. In this paper, a new method, called ARMA-PCA based MV, is proposed. It is an integrated framework of multivariable principal component analysis (PCA) and the autoregressive...
This paper analyses a variable reconstruction technique for identifying a faulty sensor. The reconstruction is associated with the application of principal component analysis (PCA) and attempts to remove "fault information" from the sensor reading. It is shown that the reconstruction (i) affects the geometry of the PCA decomposition (ii) leads to changes in the covariance matrix of the sensor...
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