An improved approach for fault detection by simultaneous overcoming of high-dimensionality, autocorrelation, and time-variability.

ABSTRACT: The control charts with the Principal Component Analysis (PCA) approach and its extension are among the data-driven methods for process monitoring and the detection of faults. Industrial processing data involves complexities such as high dimensionality, auto-correlation, and non-stationary which may occur simultaneously. An efficient fault detection technique is an approach that is robust against data training, sensitive to all the feasible faults of the process, and agile to the detection of the faults. To date, approaches such as the recursive PCA (RPCA) model and the moving-window PCA (MWPCA) model have been proposed when data is high-dimensional and non-stationary or dynamic PCA (DPCA) model and its extension have been suggested for autocorrelation data. But, using the techniques liste

SUBMITTER: Hajarian N 

PROVIDER: S-EPMC7746307 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature