• DocumentCode
    3666851
  • Title

    Vibration analysis approach for corrosion pitting detection based on SVDD and PCA

  • Author

    Yonglai Zhang;Haibo Shi;Xiaofeng Zhou;Zeyu Zheng

  • Author_Institution
    Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    1534
  • Lastpage
    1538
  • Abstract
    This study is focused on corrosion pitting on the raceways and ball in rolling bearings. We analyze 224 records in the time domain, and combine support vector data description (SVDD) with principal component analysis (PCA) algorithm to improve diagnostic accuracy. Experiment results show that the proposed method can achieve good accuracy based on an imbalanced dataset. The new method is thus well-suited for corrosion pitting detection in rolling bearings.
  • Keywords
    "Support vector machines","Vibrations","Principal component analysis","Corrosion","Rolling bearings","Accuracy","Hidden Markov models"
  • Publisher
    ieee
  • Conference_Titel
    Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on
  • Print_ISBN
    978-1-4799-8728-3
  • Type

    conf

  • DOI
    10.1109/CYBER.2015.7288173
  • Filename
    7288173
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3666851