The Turbine Machine Fault Prediction Based on Kernel Principal Component Analysis

Abstract:

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Kernel principal component analysis (KPCA) is presented and is applied to predict the huge electro-mechanical system fault. Take the gas turbine set of Beijing Yanshan Petrochemical Refinery as the research object. KPCA uses the historical normal data of vibration intensity value to establish a prediction system. And then it is used to forecast the collected data for judging whether the turbine set is in normal. The simulation experiment result indicates the effectiveness of the method and the running state can be judged as normal or not from the result. And the experiment also shows KPCA can obtain a satisfactory prediction result.

Info:

Periodical:

Advanced Materials Research (Volumes 383-390)

Edited by:

Wu Fan

Pages:

4787-4791

DOI:

10.4028/www.scientific.net/AMR.383-390.4787

Citation:

L. H. Lin et al., "The Turbine Machine Fault Prediction Based on Kernel Principal Component Analysis", Advanced Materials Research, Vols. 383-390, pp. 4787-4791, 2012

Online since:

November 2011

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Price:

$35.00

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