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Fault detection method based on dynamic structure preservation principal component analysis
ZHANG Ni, TIAN Xue-min, CAI Lian-fang
(College of Information and Control Engineering in China University of Petroleum, Qingdao 266580, China)
Abstract:
In order to make full use of the feature information of data in the chemical process, a fault detection method based on dynamic structure preservation principal component analysis was proposed to improve the performance and efficiency for fault detection. It firstly established auto-regression model through correlation analysis so that the dynamic feature sets could be obtained to characterize the original data. Furtherly, principal component analysis and locally linear embedding were fused together to obtain a new objective function. Besides,locally linear embedding algorithm could preserve the neighbor relationship between data collected. At the same time, local linear regression was used to find the projection that best approximated the mapping from high-dimensional samples to the embedding for on-line application furtherly. Statistics were constructed in the two spaces for process monitoring after feature extraction respectively. Simulation results of Tennessee Eastman process and Swiss-roll data show that DSPPCA-based method is more effective for feature extraction and process monitoring.
Key words:  dynamic structure preservation principal component analysis  manifold learning  correlation analysis  feature extraction  fault detection