Fault Diagnosis Model of Beer Fermentation Process Based on Multiway Kernel Principal Component Analysis

Ning Lv; Guang Yuan Bai; Yuan Jian Fu; Lu Qi Yan

doi:10.4028/www.scientific.net/AMM.644-650.2556

Paper Titles

The Method of Failure Data Processing Analysis of Filling System in Launching Site Based on Bayesian Method
p.2537

L-Ary Based General Attacks on Permutation-Only Image/Video Ciphers
p.2542

A New Method to Generate Test Cases by Genetic Algorithm
p.2547

Applications of Data Mining Algorithm in Equipment Fault Diagnosis
p.2551

Fault Diagnosis Model of Beer Fermentation Process Based on Multiway Kernel Principal Component Analysis
p.2556

A New Iterative Modularity-Based Method for Graph Clustering on Scalable Networks
p.2562

Service-Oriented Architecture of Distributed Telemetered Data Processing
p.2568

A Novel Hybrid LE and SVM with CV in Intrusion Detection
p.2572

Layered Model Algorithm and its Application in Fault Detection of Power Distribution Network
p.2577

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Fault Diagnosis Model of Beer Fermentation Process...

Fault Diagnosis Model of Beer Fermentation Process Based on Multiway Kernel Principal Component Analysis

Abstract:

Aiming at the limitation of the application of principal component analysis model for fault diagnosis in nonlinear time-varying process, kernel transformation theory is introduced into the data feature extraction of nonlinear space, on the basis of the periodic characteristics of the batch process, putting forward a kind of improved multi-way kernel principal component analysis fault diagnosis model, which effectively solves the nonlinear problem of process data and ensures integrity of data and information extraction. By comparing with other methods in experiment, the results show that the proposed method has good real-timing and accuracy to slow time-varying of batch process.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

2556-2561

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.2556

Citation:

Cite this paper

Online since:

September 2014

Authors:

Ning Lv*, Guang Yuan Bai, Yuan Jian Fu, Lu Qi Yan

Keywords:

Batch Process, Fault Detection, Multi-Way Kernel Principal Component Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] C. Zhang, Y. Li, Study on the fault-detection method in batch process based on statistical pattern analysis, Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 34, no. 9, pp.2103-2110, (2013).

Google Scholar

[2] S. Kumar, E.B. Martin, J. Morris, Detection of process model changes in PCA based performance monitoring, Proceedings of the American Control Conference, vol. 4, pp.2719-2724, (2002).

DOI: 10.1109/acc.2002.1025198

Google Scholar

[3] P. Nomikos and J.F. MacGregor, Monitoring batch processes using multiway principal component analysis, AIChE Journal, vol. 40, no. 8, pp.1361-1373, (1994).

DOI: 10.1002/aic.690400809

Google Scholar

[4] Y.S. Qi, P. Wang, X.J. Gao, Fault detection and diagnosis of multiphase batch process based on kernel principal component analysis-principal component analysis, Kongzhi Lilun Yu Yingyong/Control Theory and Applications, vol. 29, no. 6, pp.754-764, (2012).

DOI: 10.3724/sp.j.1087.2013.00350

Google Scholar

[5] L.P. Wang, Z.T. Yuan, X,H. Chen, Z.F. Zhou, PCA and KPCA for predicting membrane protein types，Proceedings of the 2009 WRI Global Congress on Intelligent Systems, GCIS 2009, vol. 2, pp.175-178, (2009).

DOI: 10.1109/gcis.2009.248

Google Scholar

[6] J.M. Lee, C.K. Yoo, I.B. Lee, Fault detection of batch processes using multiway kernel principal component analysis, Computers and Chemical Engineering, vol. 28, no. 9, pp.1837-1847, (2004).

DOI: 10.1016/j.compchemeng.2004.02.036

Google Scholar