Fault Diagnosis Method of Offset Printer Feeding Mechanism Based on Kernel Principal Component Analysis and K-Means Clustering

Qian Qian Xu; Hai Yan Zhang; He Ping Hou; Zhuo Fei Xu

doi:10.4028/www.scientific.net/AMM.731.395

Paper Titles

Analysis of the Application of Fungicide and Ethylene Absorbent for Extended Storage Life of Kyoho Grapes at Room Temperature
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Antimicrobial Effects of Cinnamon and Rosemary Essential Oils
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Prediction of Quality and Storage Period of Soybean in Small Packages under Different Temperature
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Fault Diagnosis Method of Offset Printer Feeding Mechanism Based on Kernel Principal Component Analysis and K-Means Clustering
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 731Fault Diagnosis Method of Offset Printer Feeding...

Fault Diagnosis Method of Offset Printer Feeding Mechanism Based on Kernel Principal Component Analysis and K-Means Clustering

Abstract:

The printing machine is a sort of large-scale equipment characterized by high speed and precision. A fault diagnosis method based on kernel principal component analysis (KPCA) and K-means clustering is developed to classify the types of feeding fault. The multidimensional and nonlinear data of printed image could be reduced by KPCA to make up the deficiency of the traditional K-means clustering method. In this paper, it is experimentally verified that the classification accuracy of the combined method is higher than the traditional clustering analysis method in feeding fault detection and diagnosis. This method provides a shortcut for the determination of fault sources and realizes multi-faults diagnosis of printing machinery efficiently

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Periodical:

Applied Mechanics and Materials (Volume 731)

Pages:

395-400

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.731.395

Citation:

Cite this paper

Online since:

January 2015

Authors:

Qian Qian Xu*, Hai Yan Zhang, He Ping Hou, Zhuo Fei Xu

Keywords:

Cluster Analysis, Feeding Fault, Kernel Principal Component Analysis

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* - Corresponding Author

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