Prognosis of Bearing Failure Based on Health State Estimation

  • Hack-Eun Kim
  • Andy C.C. Tan
  • Joseph Mathew
  • Eric Y. H. Kim
  • Byeong-Keun Choi


This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.


Support Vector Machine Fault Diagnosis Support Vector Machine Classifier Prognostic Model Rolling Element 
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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Hack-Eun Kim
    • 1
  • Andy C.C. Tan
    • 1
  • Joseph Mathew
    • 1
  • Eric Y. H. Kim
    • 1
  • Byeong-Keun Choi
    • 2
  1. 1.CRC for Integrated Engineering Asset Management, School of Engineering SystemsQueensland University of TechnologyBrisbaneAustralia
  2. 2.School of Mechanical and Aerospace EngineeringGyeongsang National UniversityTongyeongKorea, Republic of

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