Aluminum Reduction Cell’s Fault Monitoring Based on LS-SVM

Shu Ping Wang; Ya Jing Song; Hui Wang

doi:10.4028/www.scientific.net/AMR.734-737.2833

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 734-737Aluminum Reduction Cell’s Fault Monitoring Based...

Aluminum Reduction Cell’s Fault Monitoring Based on LS-SVM

Abstract:

In this paper the application of least squares support vector machine algorithm in fault diagnosis for electrolytic cell, under the four typical characteristics of classifier design such as normal condition, anode tsuga, fluctuations in liquid aluminum and lower polar distance, and comparing with BP neural network approach ,The result shows that this method is better than BP neural network.

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Periodical:

Advanced Materials Research (Volumes 734-737)

Pages:

2833-2837

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.734-737.2833

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Online since:

August 2013

Authors:

Shu Ping Wang*, Ya Jing Song, Hui Wang

Keywords:

Aluminum Reduction Cell, Fault Diagnosis, Ls-SVM

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* - Corresponding Author

References

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