Paper Title:
The Turbine Machine Fault Prediction Based on Kernel Principal Component Analysis
  Abstract

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)
Chapter
Chapter 19: Modeling, Analysis, and Simulation of Manufacturing Processes II
Edited by
Wu Fan
Pages
4787-4791
DOI
10.4028/www.scientific.net/AMR.383-390.4787
Citation
L. H. Lin, J. Ma, X. L. Xu, "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
$32.00
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