Hybrid Dynamic Principal Conponent Analysis Approach for Fault Detection in Steel Rolling Process

Huai Tao Shi; Jian Chang Liu; Long Li; Yu Zhang

doi:10.4028/www.scientific.net/AMR.219-220.1574

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 219-220Hybrid Dynamic Principal Conponent Analysis...

Hybrid Dynamic Principal Conponent Analysis Approach for Fault Detection in Steel Rolling Process

Abstract:

In traditional dynamic principal component analysis (DPCA) for fault detection, there are some drawbacks such as an excess of the number of principal components (PCs), low computational efficiency, etc. For dealing with the problem, this paper develops a hybrid dynamic principal component analysis (HDPCA) technique, this method can remove spacial and serial correlation by divide-and-conquer algorithm instead of parallel processing strategy, which can detect individual fault accurately and efficiently. The strip breaking fault in steel rolling process is used to demonstrate the improved performance of developed technique in comparison with traditional DPCA fault detection methods. It can be perceived that HDPCA algorithm has the better performance of fault detection and computational efficiency.

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Periodical:

Advanced Materials Research (Volumes 219-220)

Pages:

1574-1577

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.219-220.1574

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Online since:

March 2011

Authors:

Huai Tao Shi, Jian Chang Liu, Long Li, Yu Zhang

Keywords:

Fault Detection, Hybrid Dynamic Principal Component Analysis, Serial Correlation, Special Correlation, Steel Rolling Process, Strip Breaking

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